Image search reranking with multi-latent topical graph

— Image search reranking has attracted extensive attention. However, existing image reranking approaches deal with different features independently while ignoring the latent topics among them. It is important to mine multi-latent topic from the features to solve the image search reranking problem. In this paper, we propose a new image reranking model, named reranking with multi-latent topical graph (RMTG), which not only exploits the explicit information of local and global features, but also mines multi-latent topic from these features. We evaluate RMTG over the MSRA-MM dataset and show that RMTG outperforms several existing reranking methods.

• Type: Journal
• Year: 2013
• Where: ISCAS
• Authors: Junge Shen, Tao Mei, Qi Tian, Xinbo Gao
• Keywords: Hardware, ISCAS 2013