In the era of computational intelligence, computer vision-based techniques for robotic cognition have gained prominence. One of the important problems in computer vision is the recognition of objects in real-time environments. In this paper, we construct a SIFT-based SVM classifier and analyze its performance for real-time object recognition. Ten household objects from the CALTECH-101 dataset are chosen, and the optimal train-test ratio is identified by keeping other SVM parameters constant. The system achieves an overall accuracy of 85% by maintaining the ratio as 3:2. The difficulties faced in adapting such a classifier for real-time recognition are discussed. © Springer India 2016.
cited By 0; Conference of International Conference on Soft Computing Systems, ICSCS 2015 ; Conference Date: 20 April 2015 Through 21 April 2015; Conference Code:160689
A. Sampath, Sivaramakrishnan, A., Narayan, K., Aarthi, R., and Panigrahi, B. K., “A study of household object recognition using SIFT-based bag-of-words dictionary and SVMs”, Advances in Intelligent Systems and Computing, vol. 397, pp. 573-580, 2016.