Large scale image retrieval base on user generated content
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Document Type
Document Version
Author
Director
Baeza-Yates, Ricardo
van Zwol, Roelof
van Zwol, Roelof
Tutor
Other authors
Publication Date
Pages
109 p.
Embargo date
Citation
Olivares Ríos, X. Large scale image retrieval base on user generated content. Universitat Pompeu Fabra; 2011. handle: http://hdl.handle.net/10803/22718
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Citation
Doctoral program
Programa de doctorat en Tecnologies de la Informació i les Comunicacions
Abstract
Los sistemas online para compartir fotos proporcionan una valiosa fuente de contenidos generado por el usuario (UGC). La mayor a de los sistemas de re- cuperaci on de im agenes Web utilizan las anotaciones textuales para rankear los resultados, sin embargo estas anotaciones no s olo ilustran el contenido visual de una imagen, sino que tambi en describen situaciones subjetivas, espaciales, temporales y sociales, que complican la tarea de b usqueda basada en palabras clave. La investigaci on en esta tesis se centra en c omo mejorar la recuperaci on de im agenes en sistemas de gran escala, es decir, la Web, combinando informaci on proporcionada por los usuarios m as el contenido visual de las im agenes. En el presente trabajo se exploran distintos tipos de UGC, tales como anotaciones de texto, anotaciones visuales, y datos de click-through, as como diversas t ecnicas para combinar esta informaci on con el objetivo de mejorar la recuperaci on de im agenes usando informaci on visual. En conclusi on, la investigaci on realizada en esta tesis se centra en la impor- tancia de incluir la informaci on visual en distintas etapas de la recuperaci on de contenido. Combinando informaci on visual con otras formas de UGC, es posible mejorar signi cativamente el rendimiento de un sistema de recuperaci on de im agenes y cambiar la experiencia del usuario en la b usqueda de contenidos multimedia en la Web.
Online photo sharing systems provide a valuable source of user generated content (UGC). Most Web image retrieval systems use textual annotations to rank the results, although these annotations do not only illustrate the visual content of an image, but also describe subjective, spatial, temporal, and social dimensions, complicating the task of keyword based search. The research in this thesis is focused on how to improve the retrieval of images in large scale context , i.e. the Web, using information provided by users combined with visual content from images. Di erent forms of UGC are explored, such as textual annotations, visual annotations, and click-through-data, as well as di erent techniques to combine these data to improve the retrieval of images using visual information. In conclusion, the research conducted in this thesis focuses on the impor- tance to include visual information into various steps of the retrieval of media content. Using visual information, in combination with various forms of UGC, can signi cantly improve the retrieval performance and alter the user experience when searching for multimedia content on the Web. 1
Online photo sharing systems provide a valuable source of user generated content (UGC). Most Web image retrieval systems use textual annotations to rank the results, although these annotations do not only illustrate the visual content of an image, but also describe subjective, spatial, temporal, and social dimensions, complicating the task of keyword based search. The research in this thesis is focused on how to improve the retrieval of images in large scale context , i.e. the Web, using information provided by users combined with visual content from images. Di erent forms of UGC are explored, such as textual annotations, visual annotations, and click-through-data, as well as di erent techniques to combine these data to improve the retrieval of images using visual information. In conclusion, the research conducted in this thesis focuses on the impor- tance to include visual information into various steps of the retrieval of media content. Using visual information, in combination with various forms of UGC, can signi cantly improve the retrieval performance and alter the user experience when searching for multimedia content on the Web. 1
Keywords
information retrieval, image retrieval, user generated content, large image collection, image object retrieval, rank aggregation, visual models, sample selection
Subjects
62 - Engineering. Technology
Publisher
Universitat Pompeu Fabra
