Convoluted Neighborhood-Based Ordered-Dither Block Truncation Coding for Ear Image Retrieval

Author:

Sowmya M. N.1,Prasanna Keshava1

Affiliation:

1. VTU Research Scholar, Department of Computer Science and Engineering, Channabasavesvara Institute of Technology, Visvesvararaya Technological University (VTU), Gubbi, Tumkur, Karnataka India

Abstract

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition

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1. Research on the Evaluation of Teaching Modes of Internet Courses Based on Convolutional Neural Networks;2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE);2023-11-02

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