HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Automatic Image Annotation and Retrieval Using Hybrid Approach

Abstract : We firstly propose continuous probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, we present 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. Since the framework combines the advantages of generative and discriminative learning, it can predict semantic annotation precisely for unseen images. Finally, we conduct a series of experiments on a standard Corel dataset. The experiment results show that our approach outperforms many state-of-the-art approaches.
Document type :
Conference papers
Complete list of metadata

Cited literature [18 references]  Display  Hide  Download

Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, May 19, 2017 - 10:43:39 AM
Last modification on : Thursday, March 5, 2020 - 5:41:55 PM
Long-term archiving on: : Tuesday, August 22, 2017 - 12:58:48 AM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Zhixin Li, Weizhong Zhao, Zhiqing Li, Zhiping Shi. Automatic Image Annotation and Retrieval Using Hybrid Approach. 7th International Conference on Intelligent Information Processing (IIP), Oct 2012, Guilin, China. pp.347-356, ⟨10.1007/978-3-642-32891-6_43⟩. ⟨hal-01524985⟩



Record views


Files downloads