Please use this identifier to cite or link to this item: https://doi.org/10.1145/2461466.2461473
Title: Learning attribute-aware dictionary for image classification and search
Authors: Cai, J.
Zha, Z.-J. 
Luan, H. 
Zhang, S.
Tian, Q.
Keywords: bag-of-visual words
image classification and search
Issue Date: 2013
Source: Cai, J.,Zha, Z.-J.,Luan, H.,Zhang, S.,Tian, Q. (2013). Learning attribute-aware dictionary for image classification and search. ICMR 2013 - Proceedings of the 3rd ACM International Conference on Multimedia Retrieval : 33-40. ScholarBank@NUS Repository. https://doi.org/10.1145/2461466.2461473
Abstract: Bag-of-visual words (BoW) model has recently been well advocated for image classification and search. However, one critical limitation of existing BoW model is the lack of semantic information. To alleviate the impact of this issue, it is imperative to construct semantic-aware visual dictionary. In this paper, we propose a novel approach for learning visual word dictionary embedding intermediate-level semantics. Specifically, we first introduce an Attribute aware Dictionary Learning(AttrDL) scheme to learn multiple sub-dictionaries with specific semantic meanings. We divide training images into different sets and each represents a specific attribute. For each image set, an attribute-aware sub-vocabulary is learned. Hence, these resulting sub-vocabularies are more discriminative for semantics than the traditional vocabularies. Second, to get semantic-aware and discriminative BoW representation with the learned sub-vocabularies, we adopt the idea of L21-norm regularized sparse coding and recode the resulting sparse representation of each image. Experimental results show that the proposed scheme outperforms the state-of-the-art algorithms in both image classification and search tasks. © 2013 ACM.
Source Title: ICMR 2013 - Proceedings of the 3rd ACM International Conference on Multimedia Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/78214
ISBN: 9781450320337
DOI: 10.1145/2461466.2461473
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