Title :
Image search reranking with multi-latent topical graph
Author :
Junge Shen ; Tao Mei ; Qi Tian ; Xinbo Gao
Author_Institution :
Xidian Univ., Xi´an, China
Abstract :
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.
Keywords :
data mining; feature extraction; image recognition; image representation; MSRA-MM dataset; RMTG; global feature information; image search reranking problem; local feature information; multilatent topic mining; reranking-multilatent topical graph; Feature extraction; Information retrieval; Multimedia communication; Optimization; Semantics; Vectors; Visualization;
Conference_Titel :
Circuits and Systems (ISCAS), 2013 IEEE International Symposium on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-5760-9
DOI :
10.1109/ISCAS.2013.6571767