基于概率主题建模的图像语义学习与检索

Abstract

Abstract: In order to bridge the semantic gap existing in imageretrieval,a semantic learning model is proposed to annotate image automatically.Firstly,continuous probabilistic latent semantic analysis (PLSA) and its corresponding parameter estimation algorithm are presented.In addition,maximum penalized likelihood is adopted to solve the singularity problem of covariance matrix.Secondly,in terms of the characteristics of different modalities,the proposed probabilistic model employs continuous PLSA and traditional PLSA to model visual features and textual words respectively.The model can discover the mutual semantic topics ofthese two modalities by an asymmetric learning approach.So it predicts semanticannotation more precisely for unseen images.Finally,the experiments are conducted on two baseline Corel datasets which contain 5 000 and 31 695 images respectively.In comparison with several state-of-the-art approaches,higher accuracy and superior effectiveness of the approach are reported.

Key words: automatic image annotation, topic model, continuous PLSA, semantic learning, image retrieval

CLC Number:

TP391

LI Zhi-xin, CHEN Hong-chao, WU Jing-li, ZHOU Sheng-ming. Semantic Learning and Retrieval of Images Based on Probabilistic Topic Modeling[J].Journal of Guangxi Normal University(Natural Science Edition), 2012, 30(3): 125-134.

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