A Cascade Color Image Retrieval Framework

K. S. Gautam (), Latha Parameswaran () and Senthil Kumar Thangavel ()
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K. S. Gautam: Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering, Amrita School of Engineering
Latha Parameswaran: Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering, Amrita School of Engineering
Senthil Kumar Thangavel: Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering, Amrita School of Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 23-36 from Springer

Abstract: Abstract The search for the digital image from the repository is challenging since the volume of the image created and consumed is growing exponentially with respect to time. This makes the image retrieval an ongoing research problem. Rather than relying on metadata, analyzing the content of the image is proven to be a successful solution for retrieval. Since manually annotating the growing images is considered to be impossible. Thus the need for the hour is a framework that is capable of retrieving the similar images with less time complexity. In this paper, an image retrieval framework has been proposed to retrieve similar images from the repository. The motivation of the work is to leverage the smart nature of building, in such a way that when an asset inside the building is captured and given as query image, the user of the building should be provided with all the relevant assets inside the building. The proposed framework is built with color, shape and edge retrieval system that works in cascade approach. Since the framework works as a cascade, it is observed that the results get fine-tuned at every layer of the cascade thus increasing the precision. The novelty of the work lies in the third layer where the XOR operation is performed to check the magnitude of dissimilarity between the query and database images. Based on the above dissimilarity, the threshold has been fixed to differentiate the image of interest from other images in the repository. The performance of the proposed approach is evaluated with the manually built dataset. On evaluating the performance, it is inferred that the precision of the proposed framework is 99%.

Keywords: Image retrieval; Bitwise operator; HSV color histogram template matching; Correlation coefficient; Chi-square distance; Smart building (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-41862-5_3

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