Abstract—Object recognition is basically invariant to the dramatic changes caused in objects’ appearance such as location, size, viewpoint, illumination, occlusion and more by the variability in viewing conditions. In this paper, we employ an efficient approach for object recognition using invariant features and machine learning technique. The invariant features namely color, shape and texture invariant features of the objects are extracted separately with the aid of suitable feature extraction techniques. In the proposed approach, we integrate the color, shape and texture invariant features of the objects at the feature level, so as to improve the recognition performance. The fused feature set serves as the pattern for the forthcoming processes involved in the proposed approach. We employed the pattern recognition algorithms, like Discriminative Canonical Correlation (DCC) and attain distinct or identical results concerned with false positives. Our proposed approach is evaluated on the ALOI collection, a large collection of object images consists of 1000 objects recorded under various imaging circumstances. The experiments clearly demonstrate that our proposed approach significantly outperforms the state-of-the- art methods for combining color, shape and texture features. The proposed method is shown to be effective under a wide variety of imaging conditions.
Index Terms—Computer vision, objects recognition, feature extraction, color, shape, texture, discriminative canonical correlation (DCC).
V. N. Pawar is with A. C. Patil College of Engineering, Navi Mumbai, Maharashtra, India 410206 (e-mail: vnpawar2000@ gmail.com).
S. N. Talbar is with S. G. G. S. Institute of Engineering and Technology, Nanded, Maharashtra, India 431606 (e-mail: sntalbar@yahoo.com).
Cite: V. N. Pawar and S. N. Talbar, "Machine Learning Approach for Object Recognition," International Journal of Modeling and Optimization vol. 2, no. 5, pp. 622-628, 2012.
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