Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Image Clustering System on WWW using Web Texts
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Text document clustering based on frequent word sequences
Proceedings of the 14th ACM international conference on Information and knowledge management
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Iteratively clustering web images based on link and attribute reinforcements
Proceedings of the 13th annual ACM international conference on Multimedia
IEEE Transactions on Image Processing
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There has been recent work done in the area of search result organization for image retrieval. The main aim is to cluster the search results into semantically meaningful groups. A number of works benefited from the use of the bipartite spectral graph partitioning method [3][4]. However, the previous works mentioned use a set of keywords for each corresponding image. This will cause the bipartite spectral graph to have a high number of vertices and thus high in complexity. There is also a lack of understanding of the weights used in this method. In this paper we propose a two level reduced keywords approach for the bipartite spectral graph to reduce the complexity of bipartite spectral graph. We also propose weights for the bipartite spectral graph by using hierarchical term frequency-inverse document frequency (tf-idf). Experimental data show that this weighted bipartite spectral graph performs better than the bipartite spectral graph with a unity weight. We further exploit the tf-idf weights in merging the clusters.