Tagged image clustering via topic models

With the rapid growth of tagged images, researchers are now resorting to this high semantic textual information for image clustering, which has showed higher clustering performance compared with conventional methods using the low level visual features. However, how to bridge the gap between the semantic information and the visual information is still an open problem. In this paper, a novel topic model based framework is proposed for tagged image clustering, which consists of three steps. Firstly, the statistics between the visual features and the tag features are calculated to utilize the complementary characteristic between the two sources of information. Then the new tag feature embedded by visual information is extracted as the feature of images. Finally, typical topic model, i.e., Latent Dirichlet Allocation, is applied for image clustering. The proposed method can make full use of the tag and visual information for image clustering. Experimental results on two widely used datasets, i.e., Pascal VOC 2007 and NUS-WIDE Flickr databases, demonstrate the effectiveness of the proposed method.

[1]  Deanna Needell,et al.  Improving image clustering using sparse text and the wisdom of the crowds , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[2]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[3]  Yuhong Guo,et al.  Convex Subspace Representation Learning from Multi-View Data , 2013, AAAI.

[4]  Guillaume Pitel,et al.  Image clustering based on a shared nearest neighbors approach for tagged collections , 2008, CIVR '08.

[5]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[6]  Yi Shen,et al.  Cross-modal social image clustering and tag cleansing , 2013, J. Vis. Commun. Image Represent..

[7]  William J. Cook,et al.  Combinatorial optimization , 1997 .

[8]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[9]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[10]  Jing Hua,et al.  Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering , 2008, WWW.

[11]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[12]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[13]  Wei-Ying Ma,et al.  Hierarchical clustering of WWW image search results using visual, textual and link information , 2004, MULTIMEDIA '04.

[14]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[15]  Edward Y. Chang,et al.  Parallel Spectral Clustering in Distributed Systems , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Min-Yen Kan,et al.  Comment-based multi-view clustering of web 2.0 items , 2014, WWW.

[17]  ZissermanAndrew,et al.  The Pascal Visual Object Classes Challenge , 2015 .

[18]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[19]  David A. Forsyth,et al.  Discriminating Image Senses by Clustering with Multimodal Features , 2006, ACL.

[20]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Indexing , 1999, SIGIR Forum.

[21]  Tao Qin,et al.  Web image clustering by consistent utilization of visual features and surrounding texts , 2005, MULTIMEDIA '05.

[22]  Feiping Nie,et al.  Multi-View Clustering and Feature Learning via Structured Sparsity , 2013, ICML.