Title
Learning semantic concepts from image database with hybrid generative/discriminative approach
Abstract
Semantic gap has become a bottleneck of content-based image retrieval in recent years. In order to bridge the gap and improve the retrieval performance, automatic image annotation has emerged as a crucial problem. In this paper, a hybrid approach is proposed to learn the semantic concepts of images automatically. Firstly, we present continuous probabilistic latent semantic analysis (PLSA) and derive its corresponding Expectation-Maximization (EM) algorithm. Continuous PLSA assumes that elements are sampled from a multivariate Gaussian distribution given a latent aspect, instead of a multinomial one in traditional PLSA. Furthermore, we propose a hybrid framework which employs continuous PLSA to model visual features of images in generative learning stage and uses ensembles of classifier chains to classify the multi-label data in discriminative learning stage. Therefore, the framework can learn the correlations between features as well as the correlations between words. Since the hybrid approach combines the advantages of generative and discriminative learning, it can predict semantic annotation precisely for unseen images. Finally, we conduct the experiments on three baseline datasets and the results show that our approach outperforms many state-of-the-art approaches.
Year
DOI
Venue
2013
10.1016/j.engappai.2013.07.004
Eng. Appl. of AI
Keywords
DocType
Volume
hybrid framework,traditional PLSA,continuous probabilistic latent semantic,state-of-the-art approach,discriminative learning,image database,hybrid approach,semantic annotation,hybrid generative,semantic gap,Continuous PLSA,discriminative approach,semantic concept
Journal
26
Issue
ISSN
Citations 
9
0952-1976
10
PageRank 
References 
Authors
0.49
30
5
Name
Order
Citations
PageRank
Zhixin Li111124.43
Zhongzhi Shi22440238.03
Weizhong Zhao336323.10
Zhiqing Li4243.08
Zhenjun Tang532119.48