Web Image Clustering with Reduced Keywords and Weighted Bipartite Spectral Graph Partitioning

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.