Please use this identifier to cite or link to this item: https://doi.org/10.1145/1873951.1874332
Title: Towards a universal detector by mining concepts with small semantic gaps
Authors: Feng, J.
Zheng, Y.-T.
Yan, S. 
Keywords: concept detection
concept transfer
semantic gap
Issue Date: 2010
Source: Feng, J.,Zheng, Y.-T.,Yan, S. (2010). Towards a universal detector by mining concepts with small semantic gaps. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference : 1707-1710. ScholarBank@NUS Repository. https://doi.org/10.1145/1873951.1874332
Abstract: Can we have a universal detector that could recognize unseen objects with no training exemplars available? Such a detector is so desirable, as there are hundreds of thousands of object concepts in human vocabulary but few available labeled image examples. In this study, we attempt to build such a universal detector to predict concepts in the absence of training data. First, by considering both semantic relatedness and visual variance, we mine a set of realistic small-semantic-gap (SSG) concepts from a large-scale image corpus. Detectors of these concepts can deliver reasonably satisfactory recognition accuracies. From these distinctive visual models, we then leverage the semantic ontology knowledge and co-occurrence statistics of concepts to extend visual recognition to unseen concepts. To the best of our knowledge, this work presents the first research attempting to substantiate the semantic gap measuring of a large amount of concepts and leverage visually learnable concepts to predicate those with no training images available. Testings on NUS-WIDE dataset demonstrate that the selected concepts with small semantic gaps can be well modeled and the prediction of unseen concepts delivers promising results with comparable accuracy to preliminary training-based methods. © 2010 ACM.
Source Title: MM'10 - Proceedings of the ACM Multimedia 2010 International Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/72048
ISBN: 9781605589336
DOI: 10.1145/1873951.1874332
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