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RISS 인기검색어
- 검색키워드
- 저자 : ( Tianqiang Peng ) (검색결과 2 건)
- 무료
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- 유료
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A Cluster Priority Level Decision Method for Image Features
Tianqiang Peng,Haolin Gao 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.2
Though clustering Analysis has been developed for many years with many clustering methods come into application, image clustering is still a difficult problem. One of the most fundamental problems is that there are many kinds of image representations, and the distinguish ability of each feature is different, so their cluster effects are also different. To decide cluster priority level of different images features on a specific image dataset, the distinguish ability of three typical image features are analyzed, and a cluster discriminant index is present, which called Simplified Overall Cluster Quality is composed of cluster compaction and cluster separation. Experimental results showed the feature with best distinguish ability also possessed best discriminant index. So this index can be used to decide the priority of features for clustering images or the best feature for image cluster.
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Bag of Visual Words Method based on PLSA and Chi-Square Model for Object Category
( Yongwei Zhao ),( Tianqiang Peng ),( Bicheng Li ),( Shengcai Ke ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7
The problem of visual words` synonymy and ambiguity always exist in the conventional bag of visual words (BoVW) model based object category methods. Besides, the noisy visual words, so-called “visual stop-words” will degrade the semantic resolution of visual dictionary. In view of this, a novel bag of visual words method based on PLSA and chi-square model for object category is proposed. Firstly, Probabilistic Latent Semantic Analysis (PLSA) is used to analyze the semantic co-occurrence probability of visual words, infer the latent semantic topics in images, and get the latent topic distributions induced by the words. Secondly, the KL divergence is adopt to measure the semantic distance between visual words, which can get semantically related homoionym. Then, adaptive soft-assignment strategy is combined to realize the soft mapping between SIFT features and some homoionym. Finally, the chi-square model is introduced to eliminate the “visual stop-words” and reconstruct the visual vocabulary histograms. Moreover, SVM (Support Vector Machine) is applied to accomplish object classification. Experimental results indicated that the synonymy and ambiguity problems of visual words can be overcome effectively. The distinguish ability of visual semantic resolution as well as the object classification performance are substantially boosted compared with the traditional methods.
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