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International Journal of Computer Vision
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PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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Pattern Recognition Letters
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IEEE Transactions on Multimedia
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The paper presents a novel unsupervised contextual spectral (CSE) framework for human action and video classification. Similar to textual words, the visual word (a mid-level semantic) representation of an image or video contains a combination of synonymous words which give rise to the ambiguity of the representation. To narrow the semantic gap between visual words (mid-level semantic representation) and high-level semantics, we propose a high level representation called approximate-semantic descriptor. The experimental results show that the proposed approach for visual words disambiguation could improve the subsequent classification performance. In the paper, the approximate-semantic descriptor learning is formulated as a spectral clustering problem, such that semantically associated visual words are placed closely in low-dimensional semantic space and then clustered into one approximate-semantic descriptor. Specifically, the high level representation of human action videos is learnt by capturing the inter-video context of mid-level semantics via a non-parametric correlation measure. Experiments on four standard datasets demonstrate that our approach can achieve significantly improved results with respect to the state of the art, particularly for unconstrained environments.