Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 397, p.573-580 (2016)

ISBN:

9788132226697

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84955267547&partnerID=40&md5=e54a403c6a07d6fc29b1d8dc0a33b7bf

Keywords:

Artificial intelligence, Bag-of-visual-words, Computer vision, intelligent robots, Object recognition, Overall accuracies, Real time, Real time recognition, Real-time environment, Real-time object recognition, Recognition of objects, SIFT, Soft computing, Statistical tests

Abstract:

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.

Notes:

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

Cite this Research Publication

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.