Filling the Semantic Gap: A Genetic Programming Framework for Content-Based Image Retrieval
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1 INSTITUTE OF COMPUTING University of Campinas Filling the Semantic Gap: A Genetic Programming Framework for Content-Based Image Retrieval Ricardo da Silva Torres [email protected]
2 Outline Motivation Genetic Programming (GP) Image retrieval based on GP Data fusion Relevance Feedback Conclusions
3 Motivation: Semantic Gap
4 Motivation: Semantic Gap Beach My vacation Summer 2007 Blue sky Sky and sea
5 Motivation: Semantic Gap Similarity Value Similarity Function Simple Descriptor Feature Vector A Feature Vector B Feature Vector Extraction Algorithm Feature Vector Extraction Algorithm Image A Image B
6 Motivation Given an image database and a set of image descriptors, how to combine them to support search services? Our hypotheses: GP provides a comparable and effective framework to combine image descriptors
7 Genetic Programming Solution = GP Individual = GP Program
8 Genetic Programming Algorithm for evolution 1. Generate a initial population of individuals 2. For N generations do (a) Compute the fitness of each individual (b) Select the individuals for genetic operations (c) Apply reproduction (d) Apply crossover (e) Apply mutation
9 Genetic Programming Algorithm for evolution 1. Generate a initial population of individuals 2. For N generations do (a) Compute the fitness of each individual (b) Select the individuals for genetic operations (c) Apply reproduction (d) Apply crossover (e) Apply mutation
10 Genetic Programming Solution = Individual = Program Represented by TREES
11 Genetic Programming
12 Genetic Programming Algorithm for evolution 1. Generate a initial population of individuals 2. For N generations do (a) Compute the fitness of each individual (b) Select the individuals for genetic operations (c) Apply reproduction (d) Apply crossover (e) Apply mutation
13 Genetic Programming Reproduction
14 Genetic Programming Crossover
15 Genetic Programming Mutation
16 A genetic programming framework for content-based image retrieval Ricardo da S. Torres (UNICAMP), Alexandre X. Falcão (UNICAMP), Marcos A. Gonçalves (UFMG),, João P. Papa (UNICAMP), Baoping Zhang (VT), Weiguo Fan (VT), and Edward A. Fox (VT) Pattern Recognition Volume 42, Issue 2, February 2009, Pages Learning Semantics from Multimedia Content
17 Genetic Programming Problem: How to rank database images Solution = similarity function = GP Individual = GP program
18 Genetic Programming
19 Image Descriptor Similarity Value Similarity Function Simple Descriptor Feature Vector A Feature Vector B Feature Vector Extraction Algorithm Feature Vector Extraction Algorithm Image A Image B
20 Intelligent Image Descriptor Combination Similarity Value Similarity Function Similarity Value Similarity Value Similarity Value Simple Descriptor 1 Simple Descriptor 2 Simple Descriptor k Image A Image B
21 Intelligent Image Descriptor Combination Similarity Value GP Similarity Individual Function Similarity Value Similarity Value Similarity Value Simple Descriptor 1 Simple Descriptor 2 Simple Descriptor k Image A Image B
22 Fitness Function How well a GP individual (similarity function) rank training set images More relevant images at first positions
23 Experiments different collections: fish shape collection MPEG-7 collection different samples different fitness functions validation set time
24 Desciptors
25 Baselines
26 Without validation set
27 With validations set
28 GP individual: combination function
29
30
31 Without BAS, without validation set
32 Without BAS, with validation set
33 Image Retrieval with Relevance Feedback based on Genetic Programming Cristiano Ferreira (UNICAMP), Ricardo Torres (UNICAMP), Marcos Gonçalves (UFMG), Weiguo Fan (VT) XXIII Brazilian Symposium on Databases (SBBD) Campinas, 2008, p Best paper award
34 Relevance Feedback
35 Retrieval Process 1. User indication of query image 2. Show the initial set of images 3. While the user is not satisfied do (a) User indication of relevant images (b) Update the query pattern (c) Apply GP to discover the best individuals (d) Rank the database images (e) Show the images
36 Retrieval Process 1. User indication of query image 2. Show the initial set of images 3. While the user is not satisfied do (a) User indication of relevant images (b) Update the query pattern (c) Apply GP to discover the best individuals (d) Rank the database images (e) Show the images
37 Image Descriptor Combination
38 Training set
39 Fitness computaion
40 Retrieval Process 1. User indication of query image 2. Show the initial set of images 3. While the user is not satisfied do (a) User indication of relevant images (b) Update the query pattern (c) Apply GP to discover the best individuals (d) Rank the database images (e) Show the images
41 Voting for best images sorting Images that will be showed
42 Experiments Three image collections (FISHES, MPEG7, COREL) Evaluation measures with statistical significance tests Comparison with three (five) other methods Color, shape and texture descriptors 10 iterations images exhibited per iteration
43 Experiment 1
44 Recall x Iterations
45 Precision x Recall
46
47 Experiment 2
48 Recall x Iterations
49 Precison x Recall
50
51 References (selected) R. da S. Torres, A. X. Falcão, M. A. Goncalves, J. P. Papa, B. Zhang, W. Fan, and E. A. Fox. A Genetic Programming Framework for Content-based Image Retrieval. Pattern Recognition, 42(2): , February C. D. Ferreira, R. da S. Torres, M. A. Goncalves, and W. Fan. Image Retrieval with Relevance Feedback based on Genetic Programming. In: XXIII Simpósio Brasileiro de Banco de Dados, 2008, Campinas. SBBD, J. A. Santos, C. D. Ferreira, and R. da S. Torres. A Genetic Programming Approach for Relevance Feedback in Regionbased Image Retrieval Systems. In: SIBGRAPI 2008, 2008, Campo Grande, MS. XXI Brazilian Symposium on Computer Graphics and Image Processing, p
52 Summary Motivation Genetic Programming (GP) Image retrieval based on GP Data fusion Relevance Feedback
53 Acknowledgements: Support CAPES CNPq FAEPEX FAPESP Microsoft,
54 INSTITUTE OF COMPUTING University of Campinas Filling the Semantic Gap: A Genetic Programming Framework for Content-Based Image Retrieval Ricardo da Silva Torres [email protected] LIS Laboratory of Information Systems
55
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