Temporal Web Image Retrieval
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1 Gaël Dias a, José G. Moreno a, Adam Jatowt b, Ricardo Campos c,( Paul Martin a, Frédéric Jurie a, Youssef Chahir a ) (a) HULTECH/IMAGE/GREYC - University of Caen Basse-Normandie, France (b) TANAKA Lab - University of Kyoto, Japan (c) LIAAD-INESC TEC - Polytechnic Institute of Tomar, Portugal SPIRE/LAWEB 2012 Cartagena de Indias, Colombia October 25th
2 Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works
3 Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works
4 Information Retrieval in Time Classical IR: Given a query, Retrieve and Rank the most relevant documents. New needs in IR: Given a query, Retrieve, Rank, Filter and Organize the most relevant documents based on different dimensions. Different Dimensions in Web Search: Multi-faceted, Personnalized, Collaborative, Opinion, Freshness, Diversity, Spatial, Temporal, etc. Temporal Web Search: Retrieve, Organize and Filter the most relevant documents in terms of Temporal intents.
5 Information Retrieval in Time Classical IR: Given a query, Retrieve and Rank the most relevant documents. New needs in IR: Given a query, Retrieve, Rank, Filter and Organize the most relevant documents based on different dimensions. Different Dimensions in Web Search: Multi-faceted, Personnalized, Collaborative, Opinion, Freshness, Diversity, Spatial, Temporal, etc. Temporal Web Search: Retrieve, Organize and Filter the most relevant documents in terms of Temporal intents.
6 Information Retrieval in Time Classical IR: Given a query, Retrieve and Rank the most relevant documents. New needs in IR: Given a query, Retrieve, Rank, Filter and Organize the most relevant documents based on different dimensions. Different Dimensions in Web Search: Multi-faceted, Personnalized, Collaborative, Opinion, Freshness, Diversity, Spatial, Temporal, etc. Temporal Web Search: Retrieve, Organize and Filter the most relevant documents in terms of Temporal intents.
7 Information Retrieval in Time Classical IR: Given a query, Retrieve and Rank the most relevant documents. New needs in IR: Given a query, Retrieve, Rank, Filter and Organize the most relevant documents based on different dimensions. Different Dimensions in Web Search: Multi-faceted, Personnalized, Collaborative, Opinion, Freshness, Diversity, Spatial, Temporal, etc. Temporal Web Search: Retrieve, Organize and Filter the most relevant documents in terms of Temporal intents.
8 Temporal Information Retrieval Temporal Information Retrieval (TIR) aims to present information along its temporal dimension. One of the most important motivations for Textual TIR is the creation of timelines of major events. Some motivations for Visual TIR are to understand the evolution of a city or a place, or observe changes in person s outlook.
9 Temporal Information Retrieval Temporal Information Retrieval (TIR) aims to present information along its temporal dimension. One of the most important motivations for Textual TIR is the creation of timelines of major events. Some motivations for Visual TIR are to understand the evolution of a city or a place, or observe changes in person s outlook.
10 Temporal Information Retrieval Temporal Information Retrieval (TIR) aims to present information along its temporal dimension. One of the most important motivations for Textual TIR is the creation of timelines of major events. Some motivations for Visual TIR are to understand the evolution of a city or a place, or observe changes in person s outlook.
11 What We (would like to) Obtain (Cartagena de Indias)
12 While Google Gives Us This (Cartagena de Indias)
13 Multimedia Temporal Information Retrieval (I) Many studies have been appearing in the past 5 years based on Textual Data. Foundations of Temporal IR: (Baeza-Yates, 2005). Query Temporal Disambiguation: (Jones and Diaz, 2007), (Metzler et al., 2009). Temporal Clustering: (Alonso et al., 2009), (Campos et al., 2012). Temporal Ranking: (Kanhabua et al., 2011), (Chang et al., 2012). Temporal Language Models: (Berberich et al., 2010). Future IR: (Jatowt and Yeung, 2011), (Dias et al., 2011).
14 Multimedia Temporal Information Retrieval (I) Many studies have been appearing in the past 5 years based on Textual Data. Foundations of Temporal IR: (Baeza-Yates, 2005). Query Temporal Disambiguation: (Jones and Diaz, 2007), (Metzler et al., 2009). Temporal Clustering: (Alonso et al., 2009), (Campos et al., 2012). Temporal Ranking: (Kanhabua et al., 2011), (Chang et al., 2012). Temporal Language Models: (Berberich et al., 2010). Future IR: (Jatowt and Yeung, 2011), (Dias et al., 2011).
15 Multimedia Temporal Information Retrieval (I) Many studies have been appearing in the past 5 years based on Textual Data. Foundations of Temporal IR: (Baeza-Yates, 2005). Query Temporal Disambiguation: (Jones and Diaz, 2007), (Metzler et al., 2009). Temporal Clustering: (Alonso et al., 2009), (Campos et al., 2012). Temporal Ranking: (Kanhabua et al., 2011), (Chang et al., 2012). Temporal Language Models: (Berberich et al., 2010). Future IR: (Jatowt and Yeung, 2011), (Dias et al., 2011).
16 Multimedia Temporal Information Retrieval (I) Many studies have been appearing in the past 5 years based on Textual Data. Foundations of Temporal IR: (Baeza-Yates, 2005). Query Temporal Disambiguation: (Jones and Diaz, 2007), (Metzler et al., 2009). Temporal Clustering: (Alonso et al., 2009), (Campos et al., 2012). Temporal Ranking: (Kanhabua et al., 2011), (Chang et al., 2012). Temporal Language Models: (Berberich et al., 2010). Future IR: (Jatowt and Yeung, 2011), (Dias et al., 2011).
17 Multimedia Temporal Information Retrieval (I) Many studies have been appearing in the past 5 years based on Textual Data. Foundations of Temporal IR: (Baeza-Yates, 2005). Query Temporal Disambiguation: (Jones and Diaz, 2007), (Metzler et al., 2009). Temporal Clustering: (Alonso et al., 2009), (Campos et al., 2012). Temporal Ranking: (Kanhabua et al., 2011), (Chang et al., 2012). Temporal Language Models: (Berberich et al., 2010). Future IR: (Jatowt and Yeung, 2011), (Dias et al., 2011).
18 Multimedia Temporal Information Retrieval (I) Many studies have been appearing in the past 5 years based on Textual Data. Foundations of Temporal IR: (Baeza-Yates, 2005). Query Temporal Disambiguation: (Jones and Diaz, 2007), (Metzler et al., 2009). Temporal Clustering: (Alonso et al., 2009), (Campos et al., 2012). Temporal Ranking: (Kanhabua et al., 2011), (Chang et al., 2012). Temporal Language Models: (Berberich et al., 2010). Future IR: (Jatowt and Yeung, 2011), (Dias et al., 2011).
19 Multimedia Temporal Information Retrieval (I) Many studies have been appearing in the past 5 years based on Textual Data. Foundations of Temporal IR: (Baeza-Yates, 2005). Query Temporal Disambiguation: (Jones and Diaz, 2007), (Metzler et al., 2009). Temporal Clustering: (Alonso et al., 2009), (Campos et al., 2012). Temporal Ranking: (Kanhabua et al., 2011), (Chang et al., 2012). Temporal Language Models: (Berberich et al., 2010). Future IR: (Jatowt and Yeung, 2011), (Dias et al., 2011).
20 Multimedia Temporal Information Retrieval (II) To our knowledge, only two studies based on Visual Data have been proposed so far. Temporal classification of web images (Palermo, 2012). Temporal ephemeral clustering and classification of web images (SPIRE 2012).
21 Multimedia Temporal Information Retrieval (II) To our knowledge, only two studies based on Visual Data have been proposed so far. Temporal classification of web images (Palermo, 2012). Temporal ephemeral clustering and classification of web images (SPIRE 2012).
22 Multimedia Temporal Information Retrieval (II) To our knowledge, only two studies based on Visual Data have been proposed so far. Temporal classification of web images (Palermo, 2012). Temporal ephemeral clustering and classification of web images (SPIRE 2012).
23 Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works
24 Temporal Web Image Ephemeral Clustering (I) Ephemeral clustering is also known as Search Results Clustering (SRC). SRC algorithms deal with small data sets such as web search results (e.g. web snippets, web images). SRC algorithms focus on the dynamics and volatility of the web.
25 Temporal Web Image Ephemeral Clustering (I) Ephemeral clustering is also known as Search Results Clustering (SRC). SRC algorithms deal with small data sets such as web search results (e.g. web snippets, web images). SRC algorithms focus on the dynamics and volatility of the web.
26 Temporal Web Image Ephemeral Clustering (I) Ephemeral clustering is also known as Search Results Clustering (SRC). SRC algorithms deal with small data sets such as web search results (e.g. web snippets, web images). SRC algorithms focus on the dynamics and volatility of the web.
27 Temporal Web Image Ephemeral Clustering (II) Each web image, together with its text web snippet, contains an endogenous: thematic dimension(s), temporal dimension(s). SRC algorithms should successfully build topically and temporally related clusters of web images. A cluster should only contain web images, which focus on the same topic in the same period of time.
28 Temporal Web Image Ephemeral Clustering (II) Each web image, together with its text web snippet, contains an endogenous: thematic dimension(s), temporal dimension(s). SRC algorithms should successfully build topically and temporally related clusters of web images. A cluster should only contain web images, which focus on the same topic in the same period of time.
29 Temporal Web Image Ephemeral Clustering (II) Each web image, together with its text web snippet, contains an endogenous: thematic dimension(s), temporal dimension(s). SRC algorithms should successfully build topically and temporally related clusters of web images. A cluster should only contain web images, which focus on the same topic in the same period of time.
30 Topical Ephemeral Clustering Topical Ephemeral Clustering is also commonly named Multi-Faceted Search. Many works exist over web snippets (Carpineto et al, 2009), (Dias et al, 2011), (Scaiella et al, 2012). Works over web images have been following a three-steps procedure (Din et al, 2008), (Moreno et al, 2011). For a given query, relevant facets are embodied by the cluster labels obtained by SRC algorithms over (text) web image snippets, Topical Clustering is obtained by repetitive facet query expansion, Images with similar (topical) visual contents are maintained within clusters.
31 Topical Ephemeral Clustering Topical Ephemeral Clustering is also commonly named Multi-Faceted Search. Many works exist over web snippets (Carpineto et al, 2009), (Dias et al, 2011), (Scaiella et al, 2012). Works over web images have been following a three-steps procedure (Din et al, 2008), (Moreno et al, 2011). For a given query, relevant facets are embodied by the cluster labels obtained by SRC algorithms over (text) web image snippets, Topical Clustering is obtained by repetitive facet query expansion, Images with similar (topical) visual contents are maintained within clusters.
32 Topical Ephemeral Clustering Topical Ephemeral Clustering is also commonly named Multi-Faceted Search. Many works exist over web snippets (Carpineto et al, 2009), (Dias et al, 2011), (Scaiella et al, 2012). Works over web images have been following a three-steps procedure (Din et al, 2008), (Moreno et al, 2011). For a given query, relevant facets are embodied by the cluster labels obtained by SRC algorithms over (text) web image snippets, Topical Clustering is obtained by repetitive facet query expansion, Images with similar (topical) visual contents are maintained within clusters.
33 Topical Ephemeral Clustering Topical Ephemeral Clustering is also commonly named Multi-Faceted Search. Many works exist over web snippets (Carpineto et al, 2009), (Dias et al, 2011), (Scaiella et al, 2012). Works over web images have been following a three-steps procedure (Din et al, 2008), (Moreno et al, 2011). For a given query, relevant facets are embodied by the cluster labels obtained by SRC algorithms over (text) web image snippets, Topical Clustering is obtained by repetitive facet query expansion, Images with similar (topical) visual contents are maintained within clusters.
34 Topical Ephemeral Clustering Topical Ephemeral Clustering is also commonly named Multi-Faceted Search. Many works exist over web snippets (Carpineto et al, 2009), (Dias et al, 2011), (Scaiella et al, 2012). Works over web images have been following a three-steps procedure (Din et al, 2008), (Moreno et al, 2011). For a given query, relevant facets are embodied by the cluster labels obtained by SRC algorithms over (text) web image snippets, Topical Clustering is obtained by repetitive facet query expansion, Images with similar (topical) visual contents are maintained within clusters.
35 Topical Ephemeral Clustering Topical Ephemeral Clustering is also commonly named Multi-Faceted Search. Many works exist over web snippets (Carpineto et al, 2009), (Dias et al, 2011), (Scaiella et al, 2012). Works over web images have been following a three-steps procedure (Din et al, 2008), (Moreno et al, 2011). For a given query, relevant facets are embodied by the cluster labels obtained by SRC algorithms over (text) web image snippets, Topical Clustering is obtained by repetitive facet query expansion, Images with similar (topical) visual contents are maintained within clusters.
36 Temporal Ephemeral Clustering Temporal Ephemeral Clustering has been proposed by (Alonso et al., 2009) and (Campos et al., 2012) in the context of web snippet. Works on Temporal Ephemeral Clustering for web images do not exist. We propose a three-steps procedure: For a given query, relevant year dates are extracted based on auto-completion engines, Temporal Clustering is obtained by repetitive temporal query expansion, Images with similar temporal visual intents are maintained within clusters (Classification Problem).
37 Temporal Ephemeral Clustering Temporal Ephemeral Clustering has been proposed by (Alonso et al., 2009) and (Campos et al., 2012) in the context of web snippet. Works on Temporal Ephemeral Clustering for web images do not exist. We propose a three-steps procedure: For a given query, relevant year dates are extracted based on auto-completion engines, Temporal Clustering is obtained by repetitive temporal query expansion, Images with similar temporal visual intents are maintained within clusters (Classification Problem).
38 Temporal Ephemeral Clustering Temporal Ephemeral Clustering has been proposed by (Alonso et al., 2009) and (Campos et al., 2012) in the context of web snippet. Works on Temporal Ephemeral Clustering for web images do not exist. We propose a three-steps procedure: For a given query, relevant year dates are extracted based on auto-completion engines, Temporal Clustering is obtained by repetitive temporal query expansion, Images with similar temporal visual intents are maintained within clusters (Classification Problem).
39 Temporal Ephemeral Clustering Temporal Ephemeral Clustering has been proposed by (Alonso et al., 2009) and (Campos et al., 2012) in the context of web snippet. Works on Temporal Ephemeral Clustering for web images do not exist. We propose a three-steps procedure: For a given query, relevant year dates are extracted based on auto-completion engines, Temporal Clustering is obtained by repetitive temporal query expansion, Images with similar temporal visual intents are maintained within clusters (Classification Problem).
40 Temporal Ephemeral Clustering Temporal Ephemeral Clustering has been proposed by (Alonso et al., 2009) and (Campos et al., 2012) in the context of web snippet. Works on Temporal Ephemeral Clustering for web images do not exist. We propose a three-steps procedure: For a given query, relevant year dates are extracted based on auto-completion engines, Temporal Clustering is obtained by repetitive temporal query expansion, Images with similar temporal visual intents are maintained within clusters (Classification Problem).
41 Temporal Web Image Clustering Framework Input: TextQuery, TemporalIntentDetector j ; Output: TemporalImageClusterSet; QueryTemporalIntentSet = TemporalIntentDetector j (TextQuery); for each QueryTemporalIntent i in QueryTemporalIntentSet do ClusterYearName = getyearintent(querytemporalintent i ); ExpandedTemporalQuery = concat(textquery, ClusterYearName); TemporalImageCluster i = getimageresults(expandedtemporalquery); TemporalImageClusterName i = ClusterYearName; TemporalVisualFiltering(TemporalImageCluster i ); end for return TemporalImageClusterSet;
42 Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works Some Other Results
43 Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works
44 Principles of Temporal Classification Given a digital document (text or image), predict the creation date of the document.
45 Works on Temporal Classification Within text books, some approaches have been proposed based on Language Models (De Jong et al, 2005) and (Kanhabua et al, 2009). Within images, the only known approach is the one proposed by (Palermo et al., 2012) based on dating color images with SVM between 1930 and There are early works based on manual dating (Coe, 1983), who analyse the support (e.g. paper type, size) and visual characteristics of objects, places or people in the pictures. We should also mention a Kodak 2010 patent, which dates photos based on distinguishing marks that may appear on the back of the photo.
46 Works on Temporal Classification Within text books, some approaches have been proposed based on Language Models (De Jong et al, 2005) and (Kanhabua et al, 2009). Within images, the only known approach is the one proposed by (Palermo et al., 2012) based on dating color images with SVM between 1930 and There are early works based on manual dating (Coe, 1983), who analyse the support (e.g. paper type, size) and visual characteristics of objects, places or people in the pictures. We should also mention a Kodak 2010 patent, which dates photos based on distinguishing marks that may appear on the back of the photo.
47 Works on Temporal Classification Within text books, some approaches have been proposed based on Language Models (De Jong et al, 2005) and (Kanhabua et al, 2009). Within images, the only known approach is the one proposed by (Palermo et al., 2012) based on dating color images with SVM between 1930 and There are early works based on manual dating (Coe, 1983), who analyse the support (e.g. paper type, size) and visual characteristics of objects, places or people in the pictures. We should also mention a Kodak 2010 patent, which dates photos based on distinguishing marks that may appear on the back of the photo.
48 Works on Temporal Classification Within text books, some approaches have been proposed based on Language Models (De Jong et al, 2005) and (Kanhabua et al, 2009). Within images, the only known approach is the one proposed by (Palermo et al., 2012) based on dating color images with SVM between 1930 and There are early works based on manual dating (Coe, 1983), who analyse the support (e.g. paper type, size) and visual characteristics of objects, places or people in the pictures. We should also mention a Kodak 2010 patent, which dates photos based on distinguishing marks that may appear on the back of the photo.
49 What Can We Achieve Easily? A set of 1170 web images based on 5 city names queries extracted from Flickr. Five classes based on the evolution of photography: [1826, 1925), [1925, 1948), [1948, 1968), [1968, 1982), [1982, 2011]. Color and texture features: ScalableColor, FCTH and CEDD. 10-fold Cross-Validation for MultiClass SVM with Linear Kernel (default parameters). Average F Measure of is achieved.
50 What Can We Achieve Easily? A set of 1170 web images based on 5 city names queries extracted from Flickr. Five classes based on the evolution of photography: [1826, 1925), [1925, 1948), [1948, 1968), [1968, 1982), [1982, 2011]. Color and texture features: ScalableColor, FCTH and CEDD. 10-fold Cross-Validation for MultiClass SVM with Linear Kernel (default parameters). Average F Measure of is achieved.
51 What Can We Achieve Easily? A set of 1170 web images based on 5 city names queries extracted from Flickr. Five classes based on the evolution of photography: [1826, 1925), [1925, 1948), [1948, 1968), [1968, 1982), [1982, 2011]. Color and texture features: ScalableColor, FCTH and CEDD. 10-fold Cross-Validation for MultiClass SVM with Linear Kernel (default parameters). Average F Measure of is achieved.
52 What Can We Achieve Easily? A set of 1170 web images based on 5 city names queries extracted from Flickr. Five classes based on the evolution of photography: [1826, 1925), [1925, 1948), [1948, 1968), [1968, 1982), [1982, 2011]. Color and texture features: ScalableColor, FCTH and CEDD. 10-fold Cross-Validation for MultiClass SVM with Linear Kernel (default parameters). Average F Measure of is achieved.
53 What Can We Achieve Easily? A set of 1170 web images based on 5 city names queries extracted from Flickr. Five classes based on the evolution of photography: [1826, 1925), [1925, 1948), [1948, 1968), [1968, 1982), [1982, 2011]. Color and texture features: ScalableColor, FCTH and CEDD. 10-fold Cross-Validation for MultiClass SVM with Linear Kernel (default parameters). Average F Measure of is achieved.
54 Fine-Grained Temporal Web Image Classification (I) The idea is to find the changes of visual characteristics based on one single topic following a real-world year distribution.
55 Fine-Grained Temporal Web Image Classification (II) A set of 1093 web images about streets extracted from Flickr (The Commons) to train the model. A set of 8831 web images to test the model. 5, 23 and 36 classes from 1820 to 1999 (i.e. 36, 8 and 5 years period). Color and texture features (LAB, SIFT) with visual words discovery (for both characteristics) and spatial pyramid transformation (only for SIFT). One against the Rest SVM with Linear Kernel.
56 Fine-Grained Temporal Web Image Classification (II) A set of 1093 web images about streets extracted from Flickr (The Commons) to train the model. A set of 8831 web images to test the model. 5, 23 and 36 classes from 1820 to 1999 (i.e. 36, 8 and 5 years period). Color and texture features (LAB, SIFT) with visual words discovery (for both characteristics) and spatial pyramid transformation (only for SIFT). One against the Rest SVM with Linear Kernel.
57 Fine-Grained Temporal Web Image Classification (II) A set of 1093 web images about streets extracted from Flickr (The Commons) to train the model. A set of 8831 web images to test the model. 5, 23 and 36 classes from 1820 to 1999 (i.e. 36, 8 and 5 years period). Color and texture features (LAB, SIFT) with visual words discovery (for both characteristics) and spatial pyramid transformation (only for SIFT). One against the Rest SVM with Linear Kernel.
58 Fine-Grained Temporal Web Image Classification (II) A set of 1093 web images about streets extracted from Flickr (The Commons) to train the model. A set of 8831 web images to test the model. 5, 23 and 36 classes from 1820 to 1999 (i.e. 36, 8 and 5 years period). Color and texture features (LAB, SIFT) with visual words discovery (for both characteristics) and spatial pyramid transformation (only for SIFT). One against the Rest SVM with Linear Kernel.
59 Fine-Grained Temporal Web Image Classification (II) A set of 1093 web images about streets extracted from Flickr (The Commons) to train the model. A set of 8831 web images to test the model. 5, 23 and 36 classes from 1820 to 1999 (i.e. 36, 8 and 5 years period). Color and texture features (LAB, SIFT) with visual words discovery (for both characteristics) and spatial pyramid transformation (only for SIFT). One against the Rest SVM with Linear Kernel.
60 Results of the Second Experiment For 26, 8, 5 years period classes, Accuracy of 0.778, and are achieved. Comparatively, (Palermo et al., 2012) achieve Accuracy of for 10 years period classes on color images. Humans only reach Accuracy!
61 Results of the Second Experiment For 26, 8, 5 years period classes, Accuracy of 0.778, and are achieved. Comparatively, (Palermo et al., 2012) achieve Accuracy of for 10 years period classes on color images. Humans only reach Accuracy!
62 Results of the Second Experiment For 26, 8, 5 years period classes, Accuracy of 0.778, and are achieved. Comparatively, (Palermo et al., 2012) achieve Accuracy of for 10 years period classes on color images. Humans only reach Accuracy!
63 Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works
64 Conclusions bla
65 Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works
66 Future Works bla.
67 Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works THANK YOU! A new hard task to deal with :)
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