Quadtree decomposition based extended vector space model for image retrieval

Authors:
Vignesh Ramanathan;Shaunak Mishra;Pabitra Mitra
Affiliations:
Indian Institute of Technology, Kharagpur - 721302;Indian Institute of Technology, Kharagpur - 721302;Indian Institute of Technology, Kharagpur - 721302
Venue:
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Year:
2011

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Content based image retrieval using bag-of-regions

MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling

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Abstract

Bag of visual words approach for image retrieval does not exploit the spatial distribution of visual words in an image. Previous attempts to incorporate the spatial distribution include modification of visual vocabulary using visual phrases along with visual words and use of spatial pyramid matching (SPM) techniques for comparing two images. This paper proposes a novel extended vector space based image retrieval technique which takes into account the spatial occurrence (context) of a visual word in an image along with the co-occurrence of other visual words in a pre-defined region (block) of the image obtained by quadtree decomposition of the image up to a fixed level of resolution. Experiments show a 19.22% increase in Mean Average Precision (MAP) over the BoW approach for the Caltech 101 database.