CTMIR:A Novel Correlated Topic Model for Image Retrieval

作     者：Jian Wen TAO College of Information Engineering Zhejiang Business Technology Institute Ningbo City China Pei Fen DING College of Information Engineering Zhejiang Business Technology Institute Ningbo City China 

会议名称：《2009 Second International Workshop on Knowledge Discovery and Data Mining》

会议日期：1000年

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

关 键 词：Probabilistic Latent Semantic Analysis Latent Dirichlet Allocation Image Retrieval Similarity Measure 

摘      要：正Representation of images by the Latent Dirichlet Allocation model combined with an appropriate similarity measure is suitable for performing large scale image retrieval in a real-world *** LDA model,however,relies on the assumption that all topics are independent of each other something that is obviously not true in most *** this work we study a recently proposed model,the Correlated Topic Model（CTM）[1],in the context of large-scale image retrieval. This approach is able to explicitly model such correlations of *** experimentally evaluate the proposed retrieval approach on a real-world large-scale database consisting of more than 246,000 images and compare the performance to related approaches.