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Learning Metrics for Diversity in Instance RetrievalAuthors: Vidyadhar Rao,Ajitesh Gupta,Visesh Chari, C V Jawahar Conference: National Confernece on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2015) Date: 2015-12-16 Report no: IIIT/TR/2015/52 AbstractInstance retrieval (IR) is the problem of retrieving specific instances of a particular object, like a monument, from a collection of images. Currently, the most popular methods for IR use Bag of words (BoW) features for retrieval. However, a prominent problem for IR remains the tendency of BoW based methods to retrieve near-identical images as most relevant results. In this paper, we define diversity in IR as variation of physical properties among most relevant retrieved results for a query image. To achieve this, we propose both an ITML algorithm that re-fashions the BoW feature space into one that appreciates diversity better, and a measure to evaluate diversity in retrieval results for IR applications. Additionally, we also generate 200 hand-labeled images from the Paris dataset, for use in further research in this area. Experiments on the popular Paris dataset show that our method outperforms the standard BoW model in many cases. I Full paper: pdf Centre for Visual Information Technology |
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