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In this paper, we propose to use local feature set for image representation, which can represent variations in an object's appearance due to changing viewpoint or camera pose. It was evidenced that usually only a part of the object are appeared in common when taking a photo of an object in different view points. With comparison of local features set extracted from different positions of images, an object can be recognized when common part is appeared in two images, which take photos of one object in different view points. In this paper, we use Canonical Correlation (also known as principle or canonical angles), which can be thought of as the angles between two d-dimensional subspace, as similarity measure of local feature sets. The proposed approach is evaluated in various view-based object datasets (Coil-100 and ETH80) for object and object category recognition. Experiments show that the performance advantages of our proposed approach can be achieved over existing techniques.