Title: Content-based image retrieval with pachinko allocation model and a combination of colour, texture and text features

Authors: Ahmed Boulemden; Yamina Tlili; Hamid A. Jalab

Addresses: Badji Mokhtar Annaba University, B.P.12, Annaba, 23000, Algeria ' Badji Mokhtar Annaba University, B.P.12, Annaba, 23000, Algeria ' Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

Abstract: We present in this paper a content-based image retrieval system (CBIR) based on pachinko allocation model (PAM) and employing a combination of colour, texture and textual features. PAM is a probabilistic topic model which captures correlation not only between words in documents but also between different topics (concepts) responsible of their generation. Regardless this advantage of PAM, there is no work which explores its utility for content based image retrieval tasks. We aim to evaluate the use of PAM for CBIRs by implementing a system based on it. In this context, PAM was applied with two different modalities of features, image global features and textual indexes separately and combined. Mean average precision is evaluated. The use of PAM with combination of features has slightly improved results of using it with just one modality, this opens more perspective in order to enhance results.

Keywords: pachinko allocation; image retrieval; colour moments; texture features; global feature extraction; textual modality; features combination.

DOI: 10.1504/IJCVR.2018.091981

International Journal of Computational Vision and Robotics, 2018 Vol.8 No.2, pp.122 - 139

Received: 22 Oct 2015
Accepted: 03 Jul 2016
Published online: 24 May 2018 *

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