Sciweavers

Share
BMVC
2010

Embedding Visual Words into Concept Space for Action and Scene Recognition

8 years 6 months ago
Embedding Visual Words into Concept Space for Action and Scene Recognition
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.
Behrouz Khadem, Elahe Farahzadeh, Deepu Rajan, And
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where BMVC
Authors Behrouz Khadem, Elahe Farahzadeh, Deepu Rajan, Andrzej Sluzek
Comments (0)
books