Content Based Image Search. Mark Desnoyer
|
|
|
- Linette Thompson
- 7 years ago
- Views:
Transcription
1 Content Based Image Search Mark Desnoyer
2 Text Search - Bag of Words Gnome walks him walked house internet Gnome moon grass caught him shuttle walks PC Nord
3 Text Search - TF-IDF
4 Text Search - Query
5 Current Image Search On the web uses context around image Words around it Words in the alt tag Those words are treated as a document Same as normal text search But we want pictures, not text! Query: Horse
6 Searching With Pictures How about searching with pictures instead
7 Using Visual Words For Search Use visual words paradigm we've seen before Can use all the text search machinery we already have But, we're searching with pictures now
8 The Players O. Chum et al., Total recall: Automatic query expansion with a generative feature model for object retrieval, in Proc. ICCV, vol. 2, N. Snavely, S. M. Seitz, and R. Szeliski, Photo tourism: exploring photo collections in 3D, in International Conference on Computer Graphics and Interactive Techniques (ACM New York, NY, USA, 2006), D. Nister and H. Stewenius, Scalable recognition with a vocabulary tree, in Proc. CVPR, vol. 5, 2006.
9 SIFT Features Succinct descriptors Scale invariant Robust to changes in lighting, viewpoint, blur etc. Therefore, normally used in this search context
10 Bag of Words First Step - Build a Dictionary Must be big to be expressive enough to differentiate objects So, cluster SIFT features Each cluster is a word in the dictionary But K-means clustering 10M+ descriptors is O(NK) Hierarchical K-means (Nister) Approximate K-means (Chum)
11 Vocabulary Tree (Nister) Vocabulary Tree Copyright David Nistér, Henrik Stewénius
12 Vocabulary Tree (Nister) Vocabulary Tree Copyright David Nistér, Henrik Stewénius
13 Vocabulary Tree (Nister) Vocabulary Tree Copyright David Nistér, Henrik Stewénius
14 Vocabulary Tree (Nister) Copyright David Nistér, Henrik Stewénius
15 Vocabulary Tree (Nister) Copyright David Nistér, Henrik Stewénius
16 Approximate Nearest Neighbour (Chum) Most of the time in K-Means is spent doing Nearest Neighbour Nearest Neighbour can be approximated using kd-trees O(N logk) vs. O(NK)
17 Another Problem - Synonyms Visual Polysemy. Single visual word occurring on different (but locally similar) parts on different object categories. Visual Synonyms. Two different visual words representing a similar part of an object (wheel of a motorbike).
18 Video Google Search for recurring objects in a movie Synonyms suppressed by enforcing consistency in time Stop list used to throw out words that are too common
19 Photo Tourism Overview Scene reconstruction Photo Explorer Input photographs Relative camera positions and orientations Point cloud Sparse correspondence Copyright Noah Snavely
20 Photo Tourism Scene Recontruction Automatically estimate position, orientation, and focal length of cameras 3D positions of feature points Feature detection Pairwise feature matching Correspondence estimation Copyright Noah Snavely Incremental structure from motion
21 Photo Tourism Demo
22 Photo Tourism Limitations Matching is only performed between pairs of images Does not scale to large datasets
23 Chum et al. Experiment 5K labeled images of sights at Oxford 1M Flickr images from popular tags (distractors) Dictionary built from Oxford Images 16M descriptions -> 1M word dictionary Query for landmarks and calculate PR curve using different forms of query expansion
24 Copyright James Philbin
25 Copyright James Philbin
26 Copyright James Philbin
27 Copyright James Philbin
28 Copyright James Philbin
29 Copyright James Philbin
30 Copyright James Philbin
31 Copyright James Philbin
32 Text Query Expansion In text search, some words are similar, but they are different in the dictionary e.g. gray and grey Improve results by expanding the query to include similar words e.g. "grey goose" -> "grey goose gray" Similar words are found by clustering on document data At query time, relevant clusters are found and pulled in False positives add a lot of noise to the results
33 Image Query Expansion - Baseline
34 Transitive Closure
35 Average Query Expansion
36 Recursive Average Query Expansion
37 Multiple Image Resolution Expansion
38 Copyright James Philbin
39 Demo id=oxc1_hertford_000011
40 Results - PR Curves Before & After Expansion
41 Results - Effect Of Distractors My distractors: 9K images from searches like "building", "cathedral", "library", "historic", "spire" etc.
Recognition. Sanja Fidler CSC420: Intro to Image Understanding 1 / 28
Recognition Topics that we will try to cover: Indexing for fast retrieval (we still owe this one) History of recognition techniques Object classification Bag-of-words Spatial pyramids Neural Networks Object
TouchPaper - An Augmented Reality Application with Cloud-Based Image Recognition Service
TouchPaper - An Augmented Reality Application with Cloud-Based Image Recognition Service Feng Tang, Daniel R. Tretter, Qian Lin HP Laboratories HPL-2012-131R1 Keyword(s): image recognition; cloud service;
Building Rome in a Day
Building Rome in a Day Sameer Agarwal 1, Noah Snavely 2 Ian Simon 1 Steven. Seitz 1 Richard Szeliski 3 1 University of Washington 2 Cornell University 3 icrosoft Research Abstract We present a system that
Cees Snoek. Machine. Humans. Multimedia Archives. Euvision Technologies The Netherlands. University of Amsterdam The Netherlands. Tree.
Visual search: what's next? Cees Snoek University of Amsterdam The Netherlands Euvision Technologies The Netherlands Problem statement US flag Tree Aircraft Humans Dog Smoking Building Basketball Table
Fast Matching of Binary Features
Fast Matching of Binary Features Marius Muja and David G. Lowe Laboratory for Computational Intelligence University of British Columbia, Vancouver, Canada {mariusm,lowe}@cs.ubc.ca Abstract There has been
FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION
FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION Marius Muja, David G. Lowe Computer Science Department, University of British Columbia, Vancouver, B.C., Canada mariusm@cs.ubc.ca,
GPS-aided Recognition-based User Tracking System with Augmented Reality in Extreme Large-scale Areas
GPS-aided Recognition-based User Tracking System with Augmented Reality in Extreme Large-scale Areas Wei Guan Computer Graphics and Immersive Technologies Computer Science, USC wguan@usc.edu Suya You Computer
Distributed Kd-Trees for Retrieval from Very Large Image Collections
ALY et al.: DISTRIBUTED KD-TREES FOR RETRIEVAL FROM LARGE IMAGE COLLECTIONS1 Distributed Kd-Trees for Retrieval from Very Large Image Collections Mohamed Aly 1 malaa@vision.caltech.edu Mario Munich 2 mario@evolution.com
Image Retrieval for Image-Based Localization Revisited
SATTLER et al.: IMAGE RETRIEVAL FOR IMAGE-BASED LOCALIZATION REVISITED 1 Image Retrieval for Image-Based Localization Revisited Torsten Sattler 1 tsattler@cs.rwth-aachen.de Tobias Weyand 2 weyand@vision.rwth-aachen.de
Local features and matching. Image classification & object localization
Overview Instance level search Local features and matching Efficient visual recognition Image classification & object localization Category recognition Image classification: assigning a class label to
The Delicate Art of Flower Classification
The Delicate Art of Flower Classification Paul Vicol Simon Fraser University University Burnaby, BC pvicol@sfu.ca Note: The following is my contribution to a group project for a graduate machine learning
Recognizing Cats and Dogs with Shape and Appearance based Models. Group Member: Chu Wang, Landu Jiang
Recognizing Cats and Dogs with Shape and Appearance based Models Group Member: Chu Wang, Landu Jiang Abstract Recognizing cats and dogs from images is a challenging competition raised by Kaggle platform
Virtual Zoom: Augmented Reality on a Mobile Device
Virtual Zoom: Augmented Reality on a Mobile Device Sergey Karayev University of Washington Honors Thesis in Computer Science June 5, 2009 Abstract The world around us is photographed millions of times
System Behavior Analysis by Machine Learning
CSC456 OS Survey Yuncheng Li raingomm@gmail.com December 6, 2012 Table of contents 1 Motivation Background 2 3 4 Table of Contents Motivation Background 1 Motivation Background 2 3 4 Scenarios Motivation
State of the Art and Challenges in Crowd Sourced Modeling
Photogrammetric Week '11 Dieter Fritsch (Ed.) Wichmann/VDE Verlag, Belin & Offenbach, 2011 Frahm et al. 259 State of the Art and Challenges in Crowd Sourced Modeling JAN-MICHAEL FRAHM, PIERRE FITE-GEORGEL,
Canonical Image Selection for Large-scale Flickr Photos using Hadoop
Canonical Image Selection for Large-scale Flickr Photos using Hadoop Guan-Long Wu National Taiwan University, Taipei Nov. 10, 2009, @NCHC Communication and Multimedia Lab ( 通 訊 與 多 媒 體 實 驗 室 ), Department
Avoiding confusing features in place recognition
Avoiding confusing features in place recognition Jan Knopp 1, Josef Sivic 2, Tomas Pajdla 3 1 VISICS, ESAT-PSI, K.U. Leuven, Belgium 2 INRIA, WILLOW, Laboratoire d Informatique de l Ecole Normale Superieure,
Probabilistic Latent Semantic Analysis (plsa)
Probabilistic Latent Semantic Analysis (plsa) SS 2008 Bayesian Networks Multimedia Computing, Universität Augsburg Rainer.Lienhart@informatik.uni-augsburg.de www.multimedia-computing.{de,org} References
3D Model based Object Class Detection in An Arbitrary View
3D Model based Object Class Detection in An Arbitrary View Pingkun Yan, Saad M. Khan, Mubarak Shah School of Electrical Engineering and Computer Science University of Central Florida http://www.eecs.ucf.edu/
PHOTO BASED INTERACTION AND QUERY FOR END-USER APPLICATIONS - THE GEOPILOT PROJECT
PHOTO BASED INTERACTION AND QUERY FOR END-USER APPLICATIONS - THE GEOPILOT PROJECT Volker Paelke IKG, Institute for Cartography and Geoinformatics Leibniz Universität Hannover Appelstr. 9a, D-30167 Hannover,
Key Pain Points Addressed
Xerox Image Search 6 th International Photo Metadata Conference, London, May 17, 2012 Mathieu Chuat Director Licensing & Business Development Manager Xerox Corporation Key Pain Points Addressed Explosion
Keyframe-Based Real-Time Camera Tracking
Keyframe-Based Real-Time Camera Tracking Zilong Dong 1 Guofeng Zhang 1 Jiaya Jia 2 Hujun Bao 1 1 State Key Lab of CAD&CG, Zhejiang University 2 The Chinese University of Hong Kong {zldong, zhangguofeng,
Digital Asset Management and Controlled Vocabulary
Digital Asset Management and Controlled Vocabulary Introduction One of the challenges that DataBasics has found in delivering and implementing a digital asset management system is the issue of asset ingestion
Classifying Manipulation Primitives from Visual Data
Classifying Manipulation Primitives from Visual Data Sandy Huang and Dylan Hadfield-Menell Abstract One approach to learning from demonstrations in robotics is to make use of a classifier to predict if
IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS
IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS Alexander Velizhev 1 (presenter) Roman Shapovalov 2 Konrad Schindler 3 1 Hexagon Technology Center, Heerbrugg, Switzerland 2 Graphics & Media
<is web> Information Systems & Semantic Web University of Koblenz Landau, Germany
Information Systems University of Koblenz Landau, Germany Semantic Multimedia Management - Multimedia Annotation Tools http://isweb.uni-koblenz.de Multimedia Annotation Different levels of annotations
Clustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012
Clustering Big Data Anil K. Jain (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Outline Big Data How to extract information? Data clustering
Behavior Analysis in Crowded Environments. XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011
Behavior Analysis in Crowded Environments XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011 Behavior Analysis in Sparse Scenes Zelnik-Manor & Irani CVPR
Speaker: Prof. Mubarak Shah, University of Central Florida. Title: Representing Human Actions as Motion Patterns
Speaker: Prof. Mubarak Shah, University of Central Florida Title: Representing Human Actions as Motion Patterns Abstract: Automatic analysis of videos is one of most challenging problems in Computer vision.
The Stanford Mobile Visual Search Data Set
The Stanford Mobile Visual Search Data Set Vijay Chandrasekhar 1, David M. Chen 1, Sam S. Tsai 1, Ngai-Man Cheung 1, Huizhong Chen 1, Gabriel Takacs 1, Yuriy Reznik 2, Ramakrishna Vedantham 3, Radek Grzeszczuk
Similarity Search in a Very Large Scale Using Hadoop and HBase
http:/cedric.cnam.fr Similarity Search in a Very Large Scale Using Hadoop and HBase Rapport de recherche CEDRIC 2012 http://cedric.cnam.fr Stanislav Barton (CNAM), Vlastislav Dohnal (Masaryk University),
Machine Learning for Data Science (CS4786) Lecture 1
Machine Learning for Data Science (CS4786) Lecture 1 Tu-Th 10:10 to 11:25 AM Hollister B14 Instructors : Lillian Lee and Karthik Sridharan ROUGH DETAILS ABOUT THE COURSE Diagnostic assignment 0 is out:
Teaching Big Data by Three Levels of Projects. Abstract
Online-ISSN 2411-2933, Print-ISSN 2411-3123 July 2015 Teaching Big Data by Three Levels of Projects Jianjun Yang Department of Computer Science and Information Systems University of North Georgia jianjun.yang@ung.edu
Towards Linear-time Incremental Structure from Motion
Towards Linear-time Incremental Structure from Motion Changchang Wu University of Washington Abstract The time complexity of incremental structure from motion (SfM) is often known as O(n 4 ) with respect
Consumer video dataset with marked head trajectories
Consumer video dataset with marked head trajectories Jouni Sarvanko jouni.sarvanko@ee.oulu.fi Mika Rautiainen mika.rautiainen@ee.oulu.fi Mika Ylianttila mika.ylianttila@oulu.fi Arto Heikkinen arto.heikkinen@ee.oulu.fi
Data Mining With Big Data Image De-Duplication In Social Networking Websites
Data Mining With Big Data Image De-Duplication In Social Networking Websites Hashmi S.Taslim First Year ME Jalgaon faruki11@yahoo.co.in ABSTRACT Big data is the term for a collection of data sets which
Building Rome on a Cloudless Day
Building Rome on a Cloudless Day Jan-Michael Frahm 1, Pierre Fite-Georgel 1, David Gallup 1, Tim Johnson 1, Rahul Raguram 1, Changchang Wu 1, Yi-Hung Jen 1, Enrique Dunn 1, Brian Clipp 1, Svetlana Lazebnik
CINDOR Conceptual Interlingua Document Retrieval: TREC-8 Evaluation.
CINDOR Conceptual Interlingua Document Retrieval: TREC-8 Evaluation. Miguel Ruiz, Anne Diekema, Páraic Sheridan MNIS-TextWise Labs Dey Centennial Plaza 401 South Salina Street Syracuse, NY 13202 Abstract:
Tracking in flussi video 3D. Ing. Samuele Salti
Seminari XXIII ciclo Tracking in flussi video 3D Ing. Tutors: Prof. Tullio Salmon Cinotti Prof. Luigi Di Stefano The Tracking problem Detection Object model, Track initiation, Track termination, Tracking
Human behavior analysis from videos using optical flow
L a b o r a t o i r e I n f o r m a t i q u e F o n d a m e n t a l e d e L i l l e Human behavior analysis from videos using optical flow Yassine Benabbas Directeur de thèse : Chabane Djeraba Multitel
First-Person Activity Recognition: What Are They Doing to Me?
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 23 First-Person Activity Recognition: What Are They Doing to Me? M. S. Ryoo and Larry Matthies Jet Propulsion Laboratory,
Get Out of my Picture! Internet-based Inpainting
WHYTE et al.: GET OUT OF MY PICTURE! 1 Get Out of my Picture! Internet-based Inpainting Oliver Whyte 1 Josef Sivic 1 Andrew Zisserman 1,2 1 INRIA, WILLOW Project, Laboratoire d Informatique de l Ecole
Big Data: Image & Video Analytics
Big Data: Image & Video Analytics How it could support Archiving & Indexing & Searching Dieter Haas, IBM Deutschland GmbH The Big Data Wave 60% of internet traffic is multimedia content (images and videos)
Clustering Technique in Data Mining for Text Documents
Clustering Technique in Data Mining for Text Documents Ms.J.Sathya Priya Assistant Professor Dept Of Information Technology. Velammal Engineering College. Chennai. Ms.S.Priyadharshini Assistant Professor
CATEGORIZATION OF SIMILAR OBJECTS USING BAG OF VISUAL WORDS AND k NEAREST NEIGHBOUR CLASSIFIER
TECHNICAL SCIENCES Abbrev.: Techn. Sc., No 15(2), Y 2012 CATEGORIZATION OF SIMILAR OBJECTS USING BAG OF VISUAL WORDS AND k NEAREST NEIGHBOUR CLASSIFIER Piotr Artiemjew, Przemysław Górecki, Krzysztof Sopyła
Cloud and Mobile Computing
Cloud and Mobile Computing Protect Privacy in Offloading Yung-Hsiang Lu Electrical and Computer Engineering Purdue University Technological Trends Mobile systems become primary computing platforms for
A HYBRID APPROACH FOR RETRIEVING DIVERSE SOCIAL IMAGES OF LANDMARKS
A HYBRID APPROACH FOR RETRIEVING DIVERSE SOCIAL IMAGES OF LANDMARKS Duc-Tien Dang-Nguyen 1, Luca Piras 1, Giorgio Giacinto 1, Giulia Boato 2, Francesco G. B. De Natale 2 1 DIEE - University of Cagliari,
Image Classification for Dogs and Cats
Image Classification for Dogs and Cats Bang Liu, Yan Liu Department of Electrical and Computer Engineering {bang3,yan10}@ualberta.ca Kai Zhou Department of Computing Science kzhou3@ualberta.ca Abstract
Image Search by MapReduce
Image Search by MapReduce COEN 241 Cloud Computing Term Project Final Report Team #5 Submitted by: Lu Yu Zhe Xu Chengcheng Huang Submitted to: Prof. Ming Hwa Wang 09/01/2015 Preface Currently, there s
Group Sparse Coding. Fernando Pereira Google Mountain View, CA pereira@google.com. Dennis Strelow Google Mountain View, CA strelow@google.
Group Sparse Coding Samy Bengio Google Mountain View, CA bengio@google.com Fernando Pereira Google Mountain View, CA pereira@google.com Yoram Singer Google Mountain View, CA singer@google.com Dennis Strelow
What Makes a Great Picture?
What Makes a Great Picture? Robert Doisneau, 1955 With many slides from Yan Ke, as annotated by Tamara Berg 15-463: Computational Photography Alexei Efros, CMU, Fall 2011 Photography 101 Composition Framing
Data Mining Clustering (2) Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining
Data Mining Clustering (2) Toon Calders Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining Outline Partitional Clustering Distance-based K-means, K-medoids,
ENHANCED WEB IMAGE RE-RANKING USING SEMANTIC SIGNATURES
International Journal of Computer Engineering & Technology (IJCET) Volume 7, Issue 2, March-April 2016, pp. 24 29, Article ID: IJCET_07_02_003 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=7&itype=2
Introduction. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr
Introduction Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr What is computer vision? What does it mean, to see? The plain man's answer (and Aristotle's, too)
Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/8/2004 Hierarchical
How To Understand The Value Of Big Data
Big Data Is Not Yet Another IT Project Krish Krishnan President, Sixth Sense Advisors Inc Bridge to Big Data Oct 23 rd 2012 Background Applications, OLTP Systems, Traditional Data Warehouse and Business
Object Categorization using Co-Occurrence, Location and Appearance
Object Categorization using Co-Occurrence, Location and Appearance Carolina Galleguillos Andrew Rabinovich Serge Belongie Department of Computer Science and Engineering University of California, San Diego
View-Invariant Dynamic Texture Recognition using a Bag of Dynamical Systems
View-Invariant Dynamic Texture Recognition using a Bag of Dynamical Systems Avinash Ravichandran, Rizwan Chaudhry and René Vidal Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218,
YouTube Scale, Large Vocabulary Video Annotation
YouTube Scale, Large Vocabulary Video Annotation Nicholas Morsillo, Gideon Mann and Christopher Pal Abstract As video content on the web continues to expand, it is increasingly important to properly annotate
The use of computer vision technologies to augment human monitoring of secure computing facilities
The use of computer vision technologies to augment human monitoring of secure computing facilities Marius Potgieter School of Information and Communication Technology Nelson Mandela Metropolitan University
Discovering objects and their location in images
Discovering objects and their location in images Josef Sivic Bryan C. Russell Alexei A. Efros Andrew Zisserman William T. Freeman Dept. of Engineering Science CS and AI Laboratory School of Computer Science
Semantic Image Segmentation and Web-Supervised Visual Learning
Semantic Image Segmentation and Web-Supervised Visual Learning Florian Schroff Andrew Zisserman University of Oxford, UK Antonio Criminisi Microsoft Research Ltd, Cambridge, UK Outline Part I: Semantic
MusicGuide: Album Reviews on the Go Serdar Sali
MusicGuide: Album Reviews on the Go Serdar Sali Abstract The cameras on mobile phones have untapped potential as input devices. In this paper, we present MusicGuide, an application that can be used to
Clustering. Adrian Groza. Department of Computer Science Technical University of Cluj-Napoca
Clustering Adrian Groza Department of Computer Science Technical University of Cluj-Napoca Outline 1 Cluster Analysis What is Datamining? Cluster Analysis 2 K-means 3 Hierarchical Clustering What is Datamining?
Who are you? Learning person specific classifiers from video
Who are you? Learning person specific classifiers from video Josef Sivic, Mark Everingham 2 and Andrew Zisserman 3 INRIA, WILLOW Project, Laboratoire d Informatique de l Ecole Normale Superieure, Paris,
Digital Asset Management. Content Control for Valuable Media Assets
Digital Asset Management Content Control for Valuable Media Assets Overview Digital asset management is a core infrastructure requirement for media organizations and marketing departments that need to
Bags of Binary Words for Fast Place Recognition in Image Sequences
IEEE TRANSACTIONS ON ROBOTICS, VOL., NO., MONTH, YEAR. SHORT PAPER 1 Bags of Binary Words for Fast Place Recognition in Image Sequences Dorian Gálvez-López and Juan D. Tardós, Member, IEEE Abstract We
MetropoGIS: A City Modeling System DI Dr. Konrad KARNER, DI Andreas KLAUS, DI Joachim BAUER, DI Christopher ZACH
MetropoGIS: A City Modeling System DI Dr. Konrad KARNER, DI Andreas KLAUS, DI Joachim BAUER, DI Christopher ZACH VRVis Research Center for Virtual Reality and Visualization, Virtual Habitat, Inffeldgasse
A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow
, pp.233-237 http://dx.doi.org/10.14257/astl.2014.51.53 A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow Giwoo Kim 1, Hye-Youn Lim 1 and Dae-Seong Kang 1, 1 Department of electronices
Introduction to Data Mining
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
Cloud-Based Image Coding for Mobile Devices Toward Thousands to One Compression
IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 4, JUNE 2013 845 Cloud-Based Image Coding for Mobile Devices Toward Thousands to One Compression Huanjing Yue, Xiaoyan Sun, Jingyu Yang, and Feng Wu, Senior
High Level Describable Attributes for Predicting Aesthetics and Interestingness
High Level Describable Attributes for Predicting Aesthetics and Interestingness Sagnik Dhar Vicente Ordonez Tamara L Berg Stony Brook University Stony Brook, NY 11794, USA tlberg@cs.stonybrook.edu Abstract
Image Segmentation and Registration
Image Segmentation and Registration Dr. Christine Tanner (tanner@vision.ee.ethz.ch) Computer Vision Laboratory, ETH Zürich Dr. Verena Kaynig, Machine Learning Laboratory, ETH Zürich Outline Segmentation
Outdoors Augmented Reality on Mobile Phone using Loxel-Based Visual Feature Organization
Outdoors Augmented Reality on Mobile Phone using Loxel-Based Visual Feature Organization Gabriel Takacs Stanford University gtakacs@stanford.edu Yingen Xiong yingen.xiong@nokia.com Radek Grzeszczuk radek.grzeszczuk@nokia.com
arxiv:submit/1533655 [cs.cv] 13 Apr 2016
![arxiv:submit/1533655 [cs.cv] 13 Apr 2016](/thumbs/40/21180420.jpg "arxiv:submit/1533655 [cs.cv] 13 Apr 2016")
Bags of Local Convolutional Features for Scalable Instance Search Eva Mohedano, Kevin McGuinness and Noel E. O Connor Amaia Salvador, Ferran Marqués, and Xavier Giró-i-Nieto Insight Center for Data Analytics
MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph
MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph Janani K 1, Narmatha S 2 Assistant Professor, Department of Computer Science and Engineering, Sri Shakthi Institute of
Content-Based Image Retrieval
Content-Based Image Retrieval Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Image retrieval Searching a large database for images that match a query: What kind
ACCURACY ASSESSMENT OF BUILDING POINT CLOUDS AUTOMATICALLY GENERATED FROM IPHONE IMAGES
ACCURACY ASSESSMENT OF BUILDING POINT CLOUDS AUTOMATICALLY GENERATED FROM IPHONE IMAGES B. Sirmacek, R. Lindenbergh Delft University of Technology, Department of Geoscience and Remote Sensing, Stevinweg
The Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
Object Recognition. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr
Image Classification and Object Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Image classification Image (scene) classification is a fundamental
What is Visualization? Information Visualization An Overview. Information Visualization. Definitions
What is Visualization? Information Visualization An Overview Jonathan I. Maletic, Ph.D. Computer Science Kent State University Visualize/Visualization: To form a mental image or vision of [some
Movie Classification Using k-means and Hierarchical Clustering
Movie Classification Using k-means and Hierarchical Clustering An analysis of clustering algorithms on movie scripts Dharak Shah DA-IICT, Gandhinagar Gujarat, India dharak_shah@daiict.ac.in Saheb Motiani
SWIGS: A Swift Guided Sampling Method
IGS: A Swift Guided Sampling Method Victor Fragoso Matthew Turk University of California, Santa Barbara 1 Introduction This supplement provides more details about the homography experiments in Section.
Finding Groups of Duplicate Images in Very Large Datasets
VORAVUTHIKUNCHAI, ET AL.: FINDING GROUPS OF DUPLICATE IMAGES Finding Groups of Duplicate Images in Very Large Datasets Winn Voravuthikunchai winn.voravuthikunchai@unicaen.fr Bruno Crémilleux bruno.cremilleux@unicaen.fr
Camera geometry and image alignment
Computer Vision and Machine Learning Winter School ENS Lyon 2010 Camera geometry and image alignment Josef Sivic http://www.di.ens.fr/~josef INRIA, WILLOW, ENS/INRIA/CNRS UMR 8548 Laboratoire d Informatique,
Newton s Laws of Motion Project
Newton s Laws of Motion Project Sir Isaac Newton lived during the 1s. Like all scientists, he made observations about the world around him. Some of his observations were about motion. His observations
Footwear Print Retrieval System for Real Crime Scene Marks
Footwear Print Retrieval System for Real Crime Scene Marks Yi Tang, Sargur N. Srihari, Harish Kasiviswanathan and Jason J. Corso Center of Excellence for Document Analysis and Recognition (CEDAR) University
Open Source Tools for 3D Forensic Reconstructions Part 3 Eugene Liscio, P. Eng. November 2011
Page 1 of 7 Open Source Tools for 3D Forensic Reconstructions Part 3 Eugene Liscio, P. Eng. November 2011 In the past several years, there have been some interesting developments and availability of open
Build Panoramas on Android Phones
Build Panoramas on Android Phones Tao Chu, Bowen Meng, Zixuan Wang Stanford University, Stanford CA Abstract The purpose of this work is to implement panorama stitching from a sequence of photos taken
COMP 790-096: 096: Computational Photography
COMP 790-096: 096: Computational Photography Basic Info Instructor: Svetlana Lazebnik (lazebnik@cs.unc.edu) Office hours: By appointment, FB 244 Class webpage: http://www.cs.unc.edu/~lazebnik/fall08 Today
Flow Separation for Fast and Robust Stereo Odometry
Flow Separation for Fast and Robust Stereo Odometry Michael Kaess, Kai Ni and Frank Dellaert Abstract Separating sparse flow provides fast and robust stereo visual odometry that deals with nearly degenerate
PCL Tutorial: The Point Cloud Library By Example. Jeff Delmerico. Vision and Perceptual Machines Lab 106 Davis Hall UB North Campus. jad12@buffalo.
PCL Tutorial: The Point Cloud Library By Example Jeff Delmerico Vision and Perceptual Machines Lab 106 Davis Hall UB North Campus jad12@buffalo.edu February 11, 2013 Jeff Delmerico February 11, 2013 1/38
CS 207 - Data Science and Visualization Spring 2016
CS 207 - Data Science and Visualization Spring 2016 Professor: Sorelle Friedler sorelle@cs.haverford.edu An introduction to techniques for the automated and human-assisted analysis of data sets. These
CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING
CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING Pedro Monteiro and João Ascenso Instituto Superior Técnico - Instituto de Telecomunicações ABSTRACT In visual sensor networks, local feature
A Bayesian Framework for Unsupervised One-Shot Learning of Object Categories
A Bayesian Framework for Unsupervised One-Shot Learning of Object Categories Li Fei-Fei 1 Rob Fergus 1,2 Pietro Perona 1 1. California Institute of Technology 2. Oxford University Single Object Object
VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS
VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS Norbert Buch 1, Mark Cracknell 2, James Orwell 1 and Sergio A. Velastin 1 1. Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE,
MACHINE LEARNING IN HIGH ENERGY PHYSICS
MACHINE LEARNING IN HIGH ENERGY PHYSICS LECTURE #1 Alex Rogozhnikov, 2015 INTRO NOTES 4 days two lectures, two practice seminars every day this is introductory track to machine learning kaggle competition!
A Review of Data Mining Techniques
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,
Visual and mobile Smart Data
Copyright 2013 Augmented Vision - DFKI 06.12.2013 1 Visual and mobile Smart Data Didier Stricker didier.stricker@dfki.de Department Augmented Vision @ DFKI Head: Didier Stricker Founded in July 2008 30
A Comparison of Keypoint Descriptors in the Context of Pedestrian Detection: FREAK vs. SURF vs. BRISK
A Comparison of Keypoint Descriptors in the Context of Pedestrian Detection: FREAK vs. SURF vs. BRISK Cameron Schaeffer Stanford University CS Department camerons@stanford.edu Abstract The subject of keypoint