Wei-Jong Yang and Wei-Hau Du and Pau-Choo Chang and Jar-Ferr Yang and Pi-Hsia Hung
Visual Thing Recognition with Binary ScaleInvariant Feature Transform and Support Vector Machine Classifiers Using Color Information
789 - 793
2017
11
6
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/10007895
https://publications.waset.org/vol/126
World Academy of Science, Engineering and Technology
The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scaleinvariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With colorbased SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with colorassistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.
Open Science Index 126, 2017