Clustering web images with multi-modal features

Manjeet Rege, Ming Dong, Jing Hua
2007 Proceedings of the 15th international conference on Multimedia - MULTIMEDIA '07  
A C M, 2 0 0 9 . T h i s i s t h e a u t h o r ' s v e r s i o n o f t h e wo r k . I t i s p o s t e d h e r e b y p e r mi s s i o n o f A C M f o r y o u r p e r s o n a l u s e . N o t f o r r e d i s t r i b u t i o n . T h e d e fi n i t i v e v e r s i o n wa s p u b l i s h e d i n t h e P r o c e e d i n g s o f A C M Mu l t i me d i a 2 0 0 7 . h t t p : / / d o i . a c m. o r g / 1 0 . ABSTRACf Web image clustering has drawn significant. attention in the research community recently.
more » ... owever, not much work has been done in using multi-modal information for clustering Web im8ges. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspecth'e. In particular, ....-e modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This gra.ph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent )soperimetric High-order Co-clustering (CIHC) framework. Ahhough a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in \Veb image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this tluough experimental results performed on real Web images.
doi:10.1145/1291233.1291301 dblp:conf/mm/RegeDH07 fatcat:hdqsbwbx3zaglkqqrtfwv4t77y