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Training inter-related classifiers for automatic image classification and annotation
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DOI：10.1016/j.patcog.2012.10.029.png)
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摘要
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A structural learning algorithm is developed in this paper to achieve more effective training of large numbers of inter-related classifiers for supporting large-scale image classification and annotation. A visual concept network is constructed for characterizing the inter-concept visual correlations intuitively and determining the inter-related learning tasks automatically in the visual feature space rather than in the label space. By partitioning large numbers of object classes and image concepts into a set of groups according to their inter-concept visual correlations, the object classes and image concepts in the same group will share similar visual properties and their classifiers are strongly inter-related while the object classes and image concepts in different groups will contain various visual properties and their classifiers can be trained independently. By leveraging the inter-concept visual correlations for inter-related classifier training, our structural learning algorithm can train the inter-related classifiers jointly rather than independently, which can enhance their discrimination power significantly. Our experiments have also provided very positive results on large-scale image classification and annotation. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Inter-related classifier training
Large-scale image classification
Structural learning
Visual concept network
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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