Sparsely encoded distinctive visual features for object recognition

Hussain Dawood; Sidra Shabbir; Hassan Dawood; Nadeem Majeed; Ahmad Raza; Rubab Mehboob

doi:10.1117/1.JEI.27.6.063035

27 December 2018 Sparsely encoded distinctive visual features for object recognition

Hussain Dawood, Sidra Shabbir, Hassan Dawood, Nadeem Majeed, Ahmad Raza, Rubab Mehboob

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063035 (December 2018). https://doi.org/10.1117/1.JEI.27.6.063035

Abstract

An image feature descriptor named “sparsely encoded distinctive visual features (SEDVF)” is proposed for object recognition. SEDVF is built with the integration of local and global visual features. Visual information on edge orientation, magnitude, color, and pixel intensity is sparsely encoded by a bit plane slicing technique. Distinctive features are obtained using winner-takes-all principle. Edge gradient multiorientation detector method (EGMOD) is proposed to obtain the gradient orientations. EGMOD extracts four-directional (horizontal, vertical, and both diagonal) edge information with the proposed multioriented Scharr operator in YIQ color space. Magnitude features are extracted by incorporating chromatic information along horizontal and vertical directions in the RGB color space. SEDVF can be used as a color feature descriptor that has good discriminative power of visual features. The proposed descriptor is extensively tested for performance evaluation using K-nearest neighbor classifier on three standard datasets, including Columbia object image library, Amsterdam library of object images, and PVOC 2007, respectively. Experimental results reveal outperformance of SEDVF as compared to the state-of-the-art object recognition methods.

Citation Download Citation

Hussain Dawood, Sidra Shabbir, Hassan Dawood, Nadeem Majeed, Ahmad Raza, and Rubab Mehboob "Sparsely encoded distinctive visual features for object recognition," Journal of Electronic Imaging 27(6), 063035 (27 December 2018). https://doi.org/10.1117/1.JEI.27.6.063035

Received: 3 August 2018; Accepted: 29 November 2018; Published: 27 December 2018

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