Feature fusion for multiple view object recognition based on Wavelet Transform

This paper presents a new approach for the recognition of multiple view objects based on wavelet transform. Object recognition is achieved by extracting the energy computed from all the sub-bands of Discrete Wavelet Transform (DWT) fused with the colour moments. Therefore the proposed method considers not only the frequency relationship but also the spatial relationship of pixels in the objects. The extracted features are used as an input to the K Nearest Neighbor (K-NN) for classification. The evaluation of the system is carried on using COIL database and the performance of the proposed system is studied by varying the training set sizes. Experimental results show that the proposed method produces more accurate classification rate.