Fusing Local Image Descriptors for Large-Scale Image Retrieval

  • Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. Here we will employ the image content as a source of information to retrieve images and study the representation of images by topic models for content-based image retrieval. We focus on incorporating different types of visual descriptors into the topic modeling context. Three different fusion approaches are explored. The image representations for each fusion approach are learned in an unsupervised fashion, and each image is modeled as a mixture of topics/object parts depicted in the image. However, not all object classes will benefit from all visual descriptors. Therefore, we also investigate which visual descriptor (set) is most appropriate for each of the twelve classes under consideration. We evaluate the presented models on a real world image database consisting of moreOnline image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. Here we will employ the image content as a source of information to retrieve images and study the representation of images by topic models for content-based image retrieval. We focus on incorporating different types of visual descriptors into the topic modeling context. Three different fusion approaches are explored. The image representations for each fusion approach are learned in an unsupervised fashion, and each image is modeled as a mixture of topics/object parts depicted in the image. However, not all object classes will benefit from all visual descriptors. Therefore, we also investigate which visual descriptor (set) is most appropriate for each of the twelve classes under consideration. We evaluate the presented models on a real world image database consisting of more than 246,000 images.show moreshow less

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Metadaten
Author:Eva HörsterGND, Rainer LienhartGND
URN:urn:nbn:de:bvb:384-opus4-4041
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/502
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2007-06)
Type:Report
Language:English
Publishing Institution:Universität Augsburg
Release Date:2007/05/23
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Multimedia und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik