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Title: Fast fractal image retrieval algorithm based on HV partition

Authors: Hejin Yuan; Mingjie Li; Weihua Niu; Linna Zhang; Kebin Cui

Addresses: Department of Computer, North China Electric Power University, Baoding 071000, China ' Department of Computer, North China Electric Power University, Baoding 071000, China ' Department of Computer, North China Electric Power University, Baoding 071000, China ' Department of Computer, North China Electric Power University, Baoding 071000, China ' Department of Computer, North China Electric Power University, Baoding 071000, China

Abstract: Existing quadtree-based fractal algorithms and fractal algorithms based on horizontal vertical (HV) have the problems of long encoding time and low accuracy in the task of image retrieval. In this paper, an improved fast fractal image retrieval algorithm based on HV segmentation is proposed, which speeds up the coding time and improves the accuracy for real-time searching. In order to improve the coding efficiency, the proposed algorithm restricts R block segmentation to certain direction and location in the coding phase and uses the local codebook to find the optimal matching of the partitioned blocks. We also introduce a weighting equation calculating method of area intersection to the image matching. New weighting parameters with respect to the sizes of partitioning blocks are proposed to improve the accuracy of image retrieval. The constraint-based HV segmentation algorithm and the local codebook matching strategy are tested on the texture and Olivetti Research Laboratory (ORL) face datasets. The experimental results show that the proposed algorithm accelerates the speed of image encoding. When the recall ratio is 100%, the precision of our algorithm has improved significantly. The proposed algorithm based on HV segmentation outperforms traditional fractal search algorithms in terms of adaption adaptivity.

Keywords: HV segmentation; fractal coding; precision; image retrieval.

DOI: 10.1504/IJSPM.2020.106975

International Journal of Simulation and Process Modelling, 2020 Vol.15 No.1/2, pp.111 - 119

Received: 01 Aug 2018
Accepted: 12 May 2019

Published online: 21 Apr 2020 *

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