Title: New local sedec pattern descriptor for improving the retrieval efficiency in content-based image retrieval
Authors: S. Umamaheswaran; N. Suresh Kumar; K. Ganesh; S. Nagarajan
Addresses: Department of Computer Science and Engineering, Vickram College of Engineering, Enathi Sivagangai 630561, Tamilnadu, India ' Velammal College of Engineering and Technology, Velammal Nagar, Viraganoor, Madurai 625009, Tamilnadu, India ' Supply Chain Management - Center of Competence, McKinsey Knowledge Center India Private Limited, McKinsey & Company, Chennai, India ' Vickram College of Engineering, Enathi, Sivagangai 630561, Tamilnadu, India
Abstract: In this paper, a novel content-based image retrieval (CBIR) method is proposed using the local sedec pattern (LScP). The local binary pattern (LBP) and the local ternary pattern (LTP), encode the relationship between the referenced pixel and its surrounding pixels, by computing gray-level difference, but in a different way. The proposed methods encode the relationship between the centre pixel and its neighbours, based on directions such as vertical, horizontal and diagonal. Calculation based on first order derivatives is used here. Second order derivative is also applied to obtain LScP. The performance of the proposed method is compared with the LTrP and other local pattern (LBP, LDP and LTP) which results are obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2). Performance of the LScP shows improvement in retrieval from 75.9%/48.7% to 86.52%/54.4% in DB1, for average precision/average recall as compared with LTrP and other local patterns. A similar comparison shows improvement from 85.30% to 91.5% in terms of average retrieval rate on database DB2.
Keywords: content-based image retrieval; CBIR; local sedec patterns; LScPs; local tetra patterns; LTrPs.
International Journal of Business Information Systems, 2018 Vol.27 No.3, pp.349 - 366
Received: 10 May 2016
Accepted: 09 Aug 2016
Published online: 23 Jan 2018 *