Incorporating deep visual features into multiobjective based multi-view search results clustering

Current paper explores the use of multi-view learning for search result clustering. A web-snippet can be represented using multiple views. Apart from textual view cued by both the semantic and syntactic information, a complementary view extracted from images contained in the web-snippets is also utilized in the current framework. A single consensus partitioning is finally obtained after consulting these two individual views by the deployment of a multi-objective based clustering technique. Several objective functions including the values of a cluster quality measure evaluating the goodness of partitionings obtained using different views and an agreement-disagreement index, quantifying the amount of oneness among multiple views in generating partitionings are optimized simultaneously using AMOSA. In order to detect the number of clusters automatically, concepts of variable length solutions and a vast range of permutation operators are introduced in the clustering process. Finally a set of alternative partitionings are obtained on the final Pareto front by the proposed multi-view based multi-objective technique. Experimental results by the proposed approach on several bench-mark test datasets with respect to different performance metrics evidently establish the power of visual and text based views in achieving better search result clustering.