PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unity in Diversity: Discovering Topics from Words
Ashish Gupta and Richard Bowden
In: VISAPP 2012, 24-26 Feb 2012, Rome, Italy.


This paper presents a novel approach to learning a codebook for visual categorization, that resolves the key issue of intra-category appearance variation found in complex real world datasets. The codebook of visual-topics (semantically equivalent descriptors) is made by grouping visual-words (syntactically equivalent descriptors) that are scattered in feature space. We analyze the joint distribution of images and visual-words using information theoretic co-clustering to discover visual-topics. Our approach is compared with the standard `Bag-of-Words' approach. The statistically significant performance improvement in all the datasets utilized (Pascal VOC 2006; VOC 2007; VOC 2010; Scene-15) establishes the efficacy of our approach.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8917
Deposited By:Ashish Gupta
Deposited On:21 February 2012