A Study of Sub-Pattern Approach in 2D Shape Recognition Using the PCA and Ridgelet PCA

In the area of computer vision and machine intelligence, image recognition is a prominent field. There have been several approaches in use for 2D shape recognition using shape features extraction. This paper suggest, subspace method approach. Normally in the earlier methods proposed so far, an entire image is considered in the training and matching operation, with sub pattern approach a given image is partitioned in to many sub images. The recognition process is carried out in two steps, in the first step the Ridgelet transform is used to feature extraction, in the second step PCA is used for dimensionality reduction. For recognition efficiency rate a test study is conducted by using seventeen different distance measure technique. The training and testing process is conducted using leave-one-out strategy. The proposed method is tested on the standard MPEG-7 dataset. The results of Ridgelet PCA are compared with PCA results.