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An Efficient Image Retrieval Based on Fusion of Fast Features and Query Image Classification

An Efficient Image Retrieval Based on Fusion of Fast Features and Query Image Classification

Vibhav Prakash Singh, Subodh Srivastava, Rajeev Srivastava
Copyright: © 2017 |Volume: 4 |Issue: 1 |Pages: 19
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522515715|DOI: 10.4018/IJRSDA.2017010102
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MLA

Singh, Vibhav Prakash, et al. "An Efficient Image Retrieval Based on Fusion of Fast Features and Query Image Classification." IJRSDA vol.4, no.1 2017: pp.19-37. http://doi.org/10.4018/IJRSDA.2017010102

APA

Singh, V. P., Srivastava, S., & Srivastava, R. (2017). An Efficient Image Retrieval Based on Fusion of Fast Features and Query Image Classification. International Journal of Rough Sets and Data Analysis (IJRSDA), 4(1), 19-37. http://doi.org/10.4018/IJRSDA.2017010102

Chicago

Singh, Vibhav Prakash, Subodh Srivastava, and Rajeev Srivastava. "An Efficient Image Retrieval Based on Fusion of Fast Features and Query Image Classification," International Journal of Rough Sets and Data Analysis (IJRSDA) 4, no.1: 19-37. http://doi.org/10.4018/IJRSDA.2017010102

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Abstract

Content Based Image Retrieval (CBIR) is an emerging research area in computer vision, in which, we yield similar images as per the query content. For the implementation of CBIR system, feature extraction plays a vital role, where colour feature is quite remarkable. But, due to unevenly colored or achromatic surfaces, the role of texture is also important. In this paper, an efficient and fast CBIR system is proposed, which is based on a fusion of computationally light weighted colour and texture features; chromaticity moment, colour percentile, and local binary pattern (LBP). Using these features with multiclass classifier, the authors propose a supervised query image classification and retrieval model, which filters all irrelevant class images. Basically, this model categorizes and recovers the class of a query image based on its visual content, and this successful classification of image significantly enhances the performance and searching time of retrieval system. Descriptive experimental analysis on benchmark databases confirms the effectiveness of proposed retrieval framework.

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