A Simulated User Study of Image Browsing Using High-Level Classification
Identifieur interne : 002118 ( Istex/Corpus ); précédent : 002117; suivant : 002119A Simulated User Study of Image Browsing Using High-Level Classification
Auteurs : Teerapong Leelanupab ; Yue Feng ; Vassilios Stathopoulos ; Joemon M. JoseSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Abstract
Abstract: In this paper, we present a study of adaptive image browsing, based on high-level classification. The underlying hypothesis is that the performance of a browsing model can be improved by integrating high-level semantic concepts. We introduce a multi-label classification model designed to alleviate a binary classification problem in image classification. The effectiveness of this approach is evaluated by using a simulated user evaluation methodology. The results show that the classification assists users to narrow down the search domain and to retrieve more relevant results with respect to less amount of browsing effort.
Url:
DOI: 10.1007/978-3-642-10543-2_3
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<front><div type="abstract" xml:lang="en">Abstract: In this paper, we present a study of adaptive image browsing, based on high-level classification. The underlying hypothesis is that the performance of a browsing model can be improved by integrating high-level semantic concepts. We introduce a multi-label classification model designed to alleviate a binary classification problem in image classification. The effectiveness of this approach is evaluated by using a simulated user evaluation methodology. The results show that the classification assists users to narrow down the search domain and to retrieve more relevant results with respect to less amount of browsing effort.</div>
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<abstract xml:lang="en"><p>Abstract: In this paper, we present a study of adaptive image browsing, based on high-level classification. The underlying hypothesis is that the performance of a browsing model can be improved by integrating high-level semantic concepts. We introduce a multi-label classification model designed to alleviate a binary classification problem in image classification. The effectiveness of this approach is evaluated by using a simulated user evaluation methodology. The results show that the classification assists users to narrow down the search domain and to retrieve more relevant results with respect to less amount of browsing effort.</p>
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