NDLI: Graph-based clustering and ranking for diversified image search

Content Provider	Springer Nature Link
Author	Yan, Yan Liu, Gaowen Wang, Sen Zhang, Jian Zheng, Kai
Copyright Year	2014
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.
Starting Page	41
Ending Page	52
Page Count	12
File Format	PDF
ISSN	09424962
Journal	Multimedia Systems
Volume Number	23
Issue Number	1
e-ISSN	14321882
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-09-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Web image clustering Ranking Diversity Visibility Graph model Multimedia Information Systems Computer Communication Networks Operating Systems Data Storage Representation Data Encryption Computer Graphics
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Media Technology Hardware and Architecture Software

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