Graph-based clustering and ranking for diversified image search

Yan, Yan, Liu, Gaowen, Wang, Sen, Zhang, Jian and Zheng, Kai (2014) Graph-based clustering and ranking for diversified image search. Multimedia Systems, . doi:10.1007/s00530-014-0419-4

Author Yan, Yan
Liu, Gaowen
Wang, Sen
Zhang, Jian
Zheng, Kai
Title Graph-based clustering and ranking for diversified image search
Journal name Multimedia Systems   Check publisher's open access policy
ISSN 0942-4962
Publication date 2014-09-24
Year available 2014
Sub-type Article (original research)
DOI 10.1007/s00530-014-0419-4
Open Access Status
Total pages 12
Place of publication Heidelberg Germany
Publisher Springer
Collection year 2015
Language eng
Formatted abstract
In this paper, we consider the problem of clustering and re-ranking web image search results so as to improve diversity at high ranks. We propose a novel ranking framework, namely cluster-constrained conditional Markov random walk (CCCMRW), which has two key steps: first, cluster images into topics, and then perform Markov random walk in an image graph conditioned on constraints of image cluster information. In order to cluster the retrieval results of web images, a novel graph clustering model is proposed in this paper. We explore the surrounding text to mine the correlations between words and images and therefore the correlations are used to improve clustering results. Two kinds of correlations, namely word to image and word to word correlations, are mainly considered. As a standard text process technique, tf-idf method cannot measure the correlation of word to image directly. Therefore, we propose to combine tf-idf method with a novel feature of word, namely visibility, to infer the word-to-image correlation. By latent Dirichlet allocation model, we define a topic relevance function to compute the weights of word-to-word correlations. Taking word to image correlations as heterogeneous links and word-to-word correlations as homogeneous links, graph clustering algorithms, such as complex graph clustering and spectral co-clustering, are respectively used to cluster images into topics in this paper. In order to perform CCCMRW, a two-layer image graph is constructed with image cluster nodes as upper layer added to a base image graph. Conditioned on the image cluster information from upper layer, Markov random walk is constrained to incline to walk across different image clusters, so as to give high rank scores to images of different topics and therefore gain the diversity. Encouraging clustering and re-ranking outputs on Google image search results are reported in this paper.
Keyword Web image clustering
Graph model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 24 Sep 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: Scopus Citation Count Cited 3 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 02 Dec 2014, 00:50:53 EST by System User on behalf of School of Information Technol and Elec Engineering