ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Learning compact visual attributes for large-scale image classification
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Content-Based re-ranking of text-based image search results
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Contextual pooling in image classification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Semantic-Context-Based augmented descriptor for image feature matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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This paper presents a novel schema to address the polysemy of visual words in the widely used bag-of-words model. As a visual word may have multiple meanings, we show it is possible to use semantic contexts to disambiguate these meanings and therefore improve the performance of bag-of-words model. On one hand, for an image, multiple context-specific bag-of-words histograms are constructed, each of which corresponds to a semantic context. Then these histograms are merged by selecting only the most discriminative context for each visual word, resulting in a compact image representation. On the other hand, an image is represented by the occurrence probabilities of semantic contexts. Finally, when classifying an image, two image representations are combined at decision level to utilize the complementary information embedded in them. Experiments on three challenging image databases (PASCAL VOC 2007, Scene-15 and MSRCv2) show that our method significantly outperforms state-of-the-art classification methods.