Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation  

作　　者：Tian Dongping 田东平(Institute of Computer Software, Baoji University of Arts and Sciences, Baoji 721007, P. R. China;Institute of Computational Information Science, Baoji University of Arts and Sciences, Baoji 721007, P. R. China)

机构地区：[1]Institute of Computer Software, Baoji University of Arts and Sciences, Baoji 721007, P. R. China [2]Institute of Computational Information Science, Baoji University of Arts and Sciences, Baoji 721007, P. R. China

出　　处：《High Technology Letters》2017年第4期367-374,共8页高技术通讯（英文版）

基　　金：Supported by the National Program on Key Basic Research Project(No.2013CB329502);the National Natural Science Foundation of China(No.61202212);the Special Research Project of the Educational Department of Shaanxi Province of China(No.15JK1038);the Key Research Project of Baoji University of Arts and Sciences(No.ZK16047)

摘　　要：In recent years,multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas,especially for automatic image annotation,whose purpose is to provide an efficient and effective searching environment for users to query their images more easily. In this paper,a semi-supervised learning based probabilistic latent semantic analysis( PLSA) model for automatic image annotation is presenred. Since it's often hard to obtain or create labeled images in large quantities while unlabeled ones are easier to collect,a transductive support vector machine( TSVM) is exploited to enhance the quality of the training image data. Then,different image features with different magnitudes will result in different performance for automatic image annotation. To this end,a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible. Finally,a PLSA model with asymmetric modalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores. Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PLSA for the task of automatic image annotation.In recent years,multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas,especially for automatic image annotation,whose purpose is to provide an efficient and effective searching environment for users to query their images more easily. In this paper,a semi-supervised learning based probabilistic latent semantic analysis（ PLSA） model for automatic image annotation is presenred. Since it＇s often hard to obtain or create labeled images in large quantities while unlabeled ones are easier to collect,a transductive support vector machine（ TSVM） is exploited to enhance the quality of the training image data. Then,different image features with different magnitudes will result in different performance for automatic image annotation. To this end,a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible. Finally,a PLSA model with asymmetric modalities is constructed based on the expectation maximization（ EM） algorithm to predict a candidate set of annotations with confidence scores. Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PLSA for the task of automatic image annotation.

关 键 词：automatic image annotation semi-supervised learning probabilistic latent semantic analysis(PLSA) transductive support vector machine(TSVM) image segmentation image retrieval 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]