Neti Neti: in search of deity
16 Dec 2012-pp 30
TL;DR: Empirical evaluations demonstrate that the proposed method of image-representation and rejection cascade improves the retrieval performance on this hard problem as compared to the baseline descriptors.
Abstract: A wide category of objects and scenes can be effectively searched and classified using the modern descriptors and classifiers. With the performance on many popular categories becoming satisfactory, we explore into the issues associated with much harder recognition problems.We address the problem of searching specific images in Indian stone-carvings and sculptures in an unsupervised setup. For this, we introduce a new dataset of 524 images containing sculptures and carvings of eight different Indian deities and three other subjects popular in the Indian scenario. We perform a thorough analysis to investigate various challenges associated with this task. A new image-representation is proposed using a sequence of discriminative patches mined in an unsupervised manner. For each image, these patches are identified based on their ability to distinguish the given image from the image most dissimilar to it. Then a rejection-based re-ranking scheme is formulated based on both similarity as well as dissimilarity between two images. This new scheme is experimentally compared with two baselines using state-of-the-art descriptors on the proposed dataset. Empirical evaluations demonstrate that our proposed method of image-representation and rejection cascade improves the retrieval performance on this hard problem as compared to the baseline descriptors.
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TL;DR: Using two complementary visual retrieval methods improves both retrieval and precision performance and it is shown that Google image search can be used to query expand the name sub-set, and thereby correctly determine the full name of the sculpture.
Abstract: We describe a retrieval based method for automatically determining the title and sculptor of an imaged sculpture. This is a useful problem to solve, but also quite challenging given the variety in both form and material that sculptures can take, and the similarity in both appearance and names that can occur.Our approach is to first visually match the sculpture and then to name it by harnessing the meta-data provided by Flickr users. To this end we make the following three contributions: (i) we show that using two complementary visual retrieval methods (one based on visual words, the other on boundaries) improves both retrieval and precision performance; (ii) we show that a simple voting scheme on the tf-idf weighted meta-data can correctly hypothesize a subset of the sculpture name (provided that the meta-data has first been suitably cleaned up and normalized); and (iii) we show that Google image search can be used to query expand the name sub-set, and thereby correctly determine the full name of the sculpture.The method is demonstrated on over 500 sculptors covering more than 2000 sculptures. We also quantitatively evaluate the system and demonstrate correct identification of the sculpture on over 60% of the queries.
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"Neti Neti: in search of deity" refers background in this paper
...Previous works focussing on sculpture retrieval [1, 2] have tried to address the problem of instancebased retrieval on a collection of symbolic shapes (e....
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