10 April 2018 Approach toward extraction of sparse texture features and their application in robust two-level bare-hand detection
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Abstract
A practical two-level robust hand detection system is developed using the proposed sparse texture features and color–texture features. Traditional dense features such as a histogram of oriented gradients, Gabor, segmentation-based fractal texture analysis, and histogram features are dense in general, with high time-complexity, limiting their use in practical hand detection systems. However, if only the prominent edge or texture parameters of an image are processed, the time complexity of the system can be significantly reduced, along with an increase in performance. Performance of any practical system is most affected by the presence of spurious objects in the background. Proposed approaches use four efficient filtering techniques to extract salient regions of an image retaining significant object-related information, followed by extraction of texture features, as mentioned above. Therefore, in the first stage, 10 sparse variants of these existing texture features are extracted and assessed using five classification models, namely Naïve Bayes, Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, and SVM. In the second phase of the system, a two-level hand detection model is developed using the proposed texture and color–texture features using a sliding window-based SVM classification model. Experimental analysis shows that proposed sparse-based features are not only time efficient but also shows an improvement of 23.4% in the practical two-level detection system over the existing motion and skin filtering-based hand detection system.
© 2018 SPIE and IS&T
Songhita Misra, Songhita Misra, Rabul Hussain Laskar, Rabul Hussain Laskar, } "Approach toward extraction of sparse texture features and their application in robust two-level bare-hand detection," Journal of Electronic Imaging 27(5), 051209 (10 April 2018). https://doi.org/10.1117/1.JEI.27.5.051209 . Submission: Received: 10 December 2017; Accepted: 13 March 2018
Received: 10 December 2017; Accepted: 13 March 2018; Published: 10 April 2018
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