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.
https://hal.inria.fr/hal-01524985 Contributor : Hal IfipConnect 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