18 December 2017 Combining fine texture and coarse color features for color texture classification
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Abstract
Color texture classification plays an important role in computer vision applications because texture and color are two fundamental visual features. To classify the color texture via extracting discriminative color texture features in real time, we present an approach of combining the fine texture and coarse color features for color texture classification. First, the input image is transformed from RGB to HSV color space to separate texture and color information. Second, the scale-selective completed local binary count (CLBC) algorithm is introduced to extract the fine texture feature from the V component in HSV color space. Third, both H and S components are quantized at an optimal coarse level. Furthermore, the joint histogram of H and S components is calculated, which is considered as the coarse color feature. Finally, the fine texture and coarse color features are combined as the final descriptor and the nearest subspace classifier is used for classification. Experimental results on CUReT, KTH-TIPS, and New-BarkTex databases demonstrate that the proposed method achieves state-of-the-art classification performance. Moreover, the proposed method is fast enough for real-time applications.
© 2017 SPIE and IS&T
Junmin Wang, Yangyu Fan, Ning Li, "Combining fine texture and coarse color features for color texture classification," Journal of Electronic Imaging 26(6), 063027 (18 December 2017). https://doi.org/10.1117/1.JEI.26.6.063027 . Submission: Received: 18 August 2017; Accepted: 28 November 2017
Received: 18 August 2017; Accepted: 28 November 2017; Published: 18 December 2017
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