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In this paper we propose a novel approach to introducing semantic relations into the bag-of-words framework. We use the latent semantic models, such as LSA and pLSA, in order to define semantically-rich features and embed the visual features into a semantic space. The semantic features used in LSA technique are derived from the low-rank approximation of word-document occurrence matrix by SVD. Similarly, by using the pLSA approach, the topic-specific distributions of words can be considered dimensions of a concept space. In the proposed space, the distances between words represent the semantic distances which are used for constructing a discriminative and semantically meaningful vocabulary. We have tested our approach on the KTH action database and on the Fifteen Scene database and have achieved very promising results on both.

Embedding Visual Words into Concept Space for Action and Scene Recognition