By Topic

Learning Hierarchical Semantic Description Via Mixed-Norm Regularization for Image Understanding

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Liang Li ; Key Lab. of Intell. Inf. Process., Inst. of Comput. Technol., Beijing, China ; Shuqiang Jiang ; Qingming Huang

This paper proposes a new perspective-Vicept representation to solve the problem of visual polysemia and concept polymorphism in the large-scale semantic image understanding. Vicept characterizes the membership probability distribution between visual appearances and semantic concepts, and forms a hierarchical representation of image semantic from local to global. In the implementation, incorporating group sparse coding, visual appearance is encoded as a weighted sum of dictionary elements, which could obtain more accurate image representation with sparsity at the image level. To obtain discriminative Vicept descriptions with structural sparsity, mixed-norm regularization is adopted in the optimization problem for learning the concept membership distribution of visual appearance. Furthermore, we introduce a novel image distance measurement based on the hierarchical Vicept description, where different levels of Vicept distance are fused together by multi-level separability analysis. Finally, the wide applications of Vicept description are validated in our experiments, including large-scale semantic image search, image annotation, and semantic image re-ranking.

Published in:

Multimedia, IEEE Transactions on  (Volume:14 ,  Issue: 5 )