Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties

Abstract

In this study, a proposed descriptor based on the improved local ternary patterns (ILTP) that also uses the color properties of rice varieties is presented. Not only gray-scale intensity, but R, G, and B color components of the rice grains are considered. Combining a support vector machine (SVM) with the proposed descriptor for classification of 17 rice varieties planted in Vietnam gives an overall accuracy of 95.53%. To evaluate and compare the effectiveness of the proposed descriptor with other analysis techniques for rice varieties classification, texture descriptors based on local binary pattern and local ternary patterns are combined with SVM to classify these 17 rice varieties. Experiment results show that the classification accuracy from the SVM using the proposed descriptor is significantly higher than using ILTP or other texture descriptors from the literature. This technique can be used to build an automatic system of rice varieties identification and classification.

Color Texture Analysis: A Survey

“…Other algorithms have been proposed to improve LBPbased results, such as the orthogonal combination of LBP extended to color spaces [55], the local combination adaptive ternary pattern that encodes both color and local information [56], the improved local ternary patterns extended for color properties [57], the color local pattern (CLP) [58], the multichannel adder-based LBP and multichannel decoderbased LBP (mdLBP) [59], the softly quantized color LBP [60], the local binary pattern for color images where a color pixel is treated as a vector having m-components and form a hyperplane [61], and more recently the left to right LBP, the top to down LBP, the curve surface LBP, and the cube diagonal LBP [62], the mean distance LBP combined with color features and co-occurrence matrix [63], the multiple channels LBP that uses both the correlation information among multiple color channels and characteristics in a single color channel [64], and new descriptors called LBPL and LBPL + LBPC that represent color cue as the correlation of pixels after deriving three regression lines in a local window and deriving LBP-like patterns [65].…”