Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Elements of information theory
Elements of information theory
Clustering hypertext with applications to web searching
HYPERTEXT '00 Proceedings of the eleventh ACM on Hypertext and hypermedia
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Topic Identification Using Webpage Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ReCoM: reinforcement clustering of multi-type interrelated data objects
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Link mining: a new data mining challenge
ACM SIGKDD Explorations Newsletter
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Multi-model similarity propagation and its application for web image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IGroup: web image search results clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Bipartite graph reinforcement model for web image annotation
Proceedings of the 15th international conference on Multimedia
Correlated multi-label refinement for semantic noise removal
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Integrating hierarchical feature selection and classifier training for multi-label image annotation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Identifying points of interest by self-tuning clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Web image clustering with reduced keywords and weighted bipartite spectral graph partitioning
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Semantic image clustering using object relation network
CVM'12 Proceedings of the First international conference on Computational Visual Media
A bag-of-semantics model for image clustering
The Visual Computer: International Journal of Computer Graphics
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Image clustering is an important research topic which contributes to a wide range of applications. Traditional image clustering approaches are based on image content features only, while content features alone can hardly describe the semantics of the images. In the context of Web, images are no longer assumed homogeneous and "flatdistributed but are richly structured. There are two kinds of reinforcements embedded in such data: 1) the reinforcement between attributes of different data types (intra-type links reinforcements); and 2) the reinforcement between object attributes and the inter-type links (inter-type links reinforcements). Unfortunately, most of the previous works addressing relational data failed to fully explore the reinforcements. In this paper, we propose a reinforcement clustering framework to tackle this problem. It reinforces images and texts' attributes via inter-type links and inversely uses these attributes to update these links. The iterative reinforcing nature of this framework promises the discovery of the semantic structure of images, which is the basis of image clustering. Experimental results show the effectiveness of our proposed framework.