How Well Do Computational Features Perceptually Rank Textures? A Comparative Evaluation

Dong, Xinghui; Methven, Thomas S.; Chantler, Mike J.

Abstract

Inspired by studies [4, 23, 40] which compared rankings obtained by search engines and human observers, in this paper we compare texture rankings derived by 51 sets of computational features against perceptual texture rankings obtained from a free-grouping experiment with 30 human observers, using a unify evaluation framework. Experimental results show that the MRSAR [37], VZNEIGHBORHOOD [62], LBPHF [2] and LBPBASIC [3] feature sets perform better than their counterparts. However, none of those feature sets are ideal. The best average G and M measures (measures of ranking accuracy from 0 to 1) [15, 5] obtained are 0.36 and 0.25 respectively. We suggest that this poor performance may be due to the small local neighborhood used to calculate higher-order features which cannot capture the long-range interactions that humans have been shown to exploit [14, 16, 49, 56].

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: ACM International Conference on Multimedia Retrieval
• Start Date: Apr 1, 2014
• End Date: Apr 4, 2014
• Acceptance Date: Mar 1, 2014
• Publication Date: Apr 1, 2014
• Deposit Date: Oct 23, 2018
• Publisher: Association for Computing Machinery (ACM)
• Pages: 815-824
• Book Title: ICMR '14 Proceedings of International Conference on Multimedia Retrieval
• ISBN: 9781450327824
• DOI: https://doi.org/10.1145/2578726.2578762
• Keywords: Computational features, Evaluation, Perceptual texture ranking, Texture ranking, Texture retrieval, Texture similarity,
• Public URL: http://researchrepository.napier.ac.uk/Output/1320794