1,721,095 research outputs found
Cooperative classification of shared images
Full text linkClaudio Cusano, Simone Santini, "Cooperative classification of shared images", Proc. SPIE 7540, Imaging and Printing in a Web 2.0 World; and Multimedia Content Access: Algorithms and Systems IV, 75400T (2010) Copyright 2010 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.We propose a method the for semi-automatic organization of photo albums. The method analyzes how different users organize their own pictures. The goal is to help the user in dividing his pictures into groups characterized by a similar semantic content. The method is semi-automatic: the user starts to assign labels to the pictures and unlabeled pictures are tagged with proposed labels. The user can accept the recommendation or made a correction. We use a suitable feature representation of the images to model the different classes that the users have collected. Then, we look for correspondences between the criteria used by the different users which are integrated using boosting. A quantitative evaluation of the proposed approach is obtained by simulating the amount of user interaction needed to annotate the albums of a set of members of the flickr R(trademark) photo-sharing community
Spatially organized visualization of image query results
Full text linkGianluigi Ciocca, Claudio Cusano, Simone Santini, Raimondo Schettini, "Spatially organized visualization of image query results", Proceedings of SPIE 7881, Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems V. Ed. David Akopian, Reiner Creutzburg, Cees G. M. Snoek, Nicu Sebe, Lyndon Kennedy, SPIE (2011). Copyright 2011 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.In this work we present a system which visualizes the results obtained from image search engines in such a way that users can conveniently browse the retrieved images. The way in which search results are presented allows the user to grasp the composition of the set of images "at a glance". To do so, images are grouped and positioned according to their distribution in a prosemantic feature space which encodes information about their content at an abstraction level that can be placed between visual and semantic information. The compactness of the feature space allows a fast analysis of the image distribution so that all the computation can be performed in real time
Incremental context creation and its effects on semantic query precision
Full text linkThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-10543-2_19Proceedings of 4th International Conference on Semantic and Digital Media Technologies, SAMT 2009 Graz, Austria, December 2-4, 2009We briefly describe the results of an experimental study on the incremental creation of context out of the results of targeted queries, and discuss the increase in retrieval precision that results from the incremental enrichment of context.This work was supported in part by Consejería de Educación, Comunidad Autónoma de
Madrid, under the grant CCG08-UAM/TIC/4303, Búsqueda basada en contexto como alternativa
semántica al modelo ontológico. Simone Santini was in part supported by the Ramón
y Cajal initiative of the Ministero de educación y ciencia. Alexandra Dumitrescu was in part
supported by the European Social Fund, Universidad Autónoma de Madrid
Benchmarking without ground truth
Full text linkSimone Santini, "Benchmarking without ground truth", Proc. SPIE 6061, Internet Imaging VII, 60610I (2006). Copyright 2006 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibitedMany evaluation techniques for content based image retrieval are based on the availability of a ground truth, that is on a "correct" categorization of images so that, say, if the query image is of category A, only the returned images in category A will be considered as "hits." Based on such a ground truth, standard information retrieval measures such as precision and recall and given and used to evaluate and compare retrieval algorithms. Coherently, the assemblers of benchmarking data bases go to a certain length to have their images categorized. The assumption of the existence of a ground truth is, in many respect, naive. It is well known that the categorization of the images depends on the a priori (from the point of view of such categorization) subdivision of the semantic field in which the images are placed (a trivial observation: a plant subdivision for a botanist is very different from that for a layperson). Even within a given semantic field, however, categorization by human subjects is subject to uncertainty, and it makes little statistical sense to consider the categorization given by one person as the unassailable ground truth. In this paper I propose two evaluation techniques that apply to the case in which the ground truth is subject to uncertainty. In this case, obviously, measures such as precision and recall as well will be subject to uncertainty. The paper will explore the relation between the uncertainty in the ground truth and that in the most commonly used evaluation measures, so that the measurements done on a given system can preserve statistical significance
With a little help from my friends: Community-based assisted organization of personal photographs
Full text linkThe final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1096-yIn this paper, we propose a content-based method the for semi-automatic organization of photo albums based on the analysis of how different users organize their own pictures. The goal is to help the user in dividing his pictures into groups characterized by a similar semantic content. The method is semi-automatic: the user starts to assign labels to the pictures and unlabeled pictures are tagged with proposed labels. The user can accept the recommendation or made a correction. To formulate the suggestions is exploited the knowledge encoded in how other users have partitioned their images. The method is conceptually articulated in two parts. First, we use a suitable feature representation of the images to model the different classes that the users have collected, second, we look for correspondences between the criteria used by the different users. Boosting is used to integrate the information provided by the analysis of multiple users. A quantitative evaluation of the proposed approach is obtained by simulating the amount of user interaction needed to annotate the albums of a set of members of the flickr® photo-sharing community
High-Level Features for Image Indexing and RetrievalEncyclopedia of Information Science and Technology, Third Edition
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