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Temporal Web Image Retrieval Gal Dias a , Jos G. Moreno a , Adam - PowerPoint PPT Presentation

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Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works Temporal Web Image Retrieval Gal Dias a , Jos G. Moreno a , Adam Jatowt b , Ricardo Campos c ,( Paul Martin a , Frdric Jurie a ,

Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works Temporal Web Image Retrieval 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 Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works Outline Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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. Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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. Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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. Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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. Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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. Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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. Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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. Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works What We (would like to) Obtain (Cartagena de Indias) Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works While Google Gives Us This (Cartagena de Indias) Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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). Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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). Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval
Context Temporal Web Image Ephemeral Clustering Temporal Web Image Classification Conclusions Future Works 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). Gaël Dias, José G. Moreno, Adam Jatowt, Ricardo Campos, et al. HULTECH/IMAGE/GREYC, TANAKA Lab, LIAAD-INESC TEC Temporal Web Image Retrieval

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