Image Recommendation Based on ANOVA Cosine Similarity

Sejal, D and Ganeshsingh, T and Venugopal, KR and Iyengar, SS and Patnaik, LM (2016) Image Recommendation Based on ANOVA Cosine Similarity. In: 12th International Conference on Communication Networks (ICCN) / 12th International Conference on Data Mining and Warehousing (ICDMW) / 12th International Conference on Image and Signal Processing (ICISP), AUG 19-21, 2016, Bangalore, INDIA, pp. 562-567.

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Abstract

Online shopping is very popular and has grown exponentially due to revolution in digitization. It is a fundamental requirement of all the search engines to provide recommendation to identify user preferences. In this paper, we have proposed an algorithm to recommend images based on ANOVA Cosine Similarity where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p-value by considering image visual features on text-based search. Expanded queries are generated for user input query and text based search is performed to get initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain specific site. Experiments results shows that the ACSIR outperforms iLike method by providing more relevant products to the user input query. (C) 2016 Published by Elsevier B.V.

Item Type:	Conference Proceedings
Series.:	Procedia Computer Science
Additional Information:	Copy right for this article belongs to the ELSEVIER SCIENCE BV, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	07 Dec 2016 06:08
Last Modified:	07 Dec 2016 06:08
URI:	http://eprints.iisc.ac.in/id/eprint/55580

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