A Survey on Emotional Semantic Mapping in Image Retrieval

Liu, Zeng Rong; Li, Zhi

doi:10.4028/www.scientific.net/amr.532-533.1297

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2012

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“…Although this process is accomplished quickly and automatically, the results are seldom semantically relative to the query example due to the notorious semantic gap [1]. As a result, automatic image annotation has emerged as a crucial problem for semantic image retrieval [2,3].…”

Section: Introductionmentioning

confidence: 99%

Combining Generative/Discriminative Learning for Automatic Image Annotation and Retrieval

Li¹,

Tang²,

Zhao³

et al. 2012

IJIS

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In order to bridge the semantic gap exists in image retrieval, this paper propose an approach combining generative and discriminative learning to accomplish the task of automatic image annotation and retrieval. We firstly present continuous probabilistic latent semantic analysis (PLSA) to model continuous quantity. 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. 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.

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Section: Introductionmentioning

confidence: 99%