Title: Statistical tree-based feature vector for content-based image retrieval

Authors: Sushila Aghav-Palwe; Dhirendra Mishra

Addresses: School of Computer Engineering and Technology, MIT World Peace University, Pune, Maharashtra, India ' Mukesh Patel School of Technology Management and Engineering, Computer Engineering SVKM's NMIMS, Mumbai, India

Abstract: The efficiency of any content-based image retrieval system depends on the extracted feature vectors of individual images stored in the database. Generation of a compact feature vector with good discriminative power is a real challenge in the image retrieval system. This paper presents the experimentation carried out to generate compact feature vectors for a colour image retrieval system based on image content. It has two stages of operation. In first stage the energy compaction property of image transforms is used whereas in the second stage, the statistical tree approach is used for feature vector generation. Performance of image retrieval is tested using image feature database as per various performance evaluation parameters such as precision recall crossover point (PRCP) along with newly proposed conflicting string of images (CSI). With different colour spaces, image transforms and statistical measures, proposed approach achieves the reduction in the feature vector size with better discriminative power.

Keywords: statistical tree; image retrieval; image transform; feature extraction; low level features.

DOI: 10.1504/IJCSE.2020.106868

International Journal of Computational Science and Engineering, 2020 Vol.21 No.4, pp.556 - 563

Received: 07 Aug 2018
Accepted: 02 Apr 2019
Published online: 24 Apr 2020 *

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