Tag mining is important as far as image search engines/databases are concerned viz. Flicker, Picasa, Facebook...etc. Tag Mining is a difficult and highly relevant machine task. In this paper, we present a new approach to hybrid features based object mining and tagging that identifies the objects with higher accuracy from an occluded image. In existing system tag Mining with algorithms based on ‘Nearest neighbor classification’ have achieved considerable attention implementation point of view but at the cost of increasing computational complexity both during training and testing. It is very difficult to identify the object which is occluded in image. The objective of object tagging of image is to search over user contributed photo online which have accumulated rich human knowledge and billions of photos, then associate surrounding tags from those visually similar photos for the unlabeled image. For an unlabeled image, photos in the social media are extracted by the Feature based object tagging of image, the annotations associated with the images are expanded, and then each object group is classify. In this paper different features and classifier are compare with advantages and disadvantages.

Image processing, Object recognition, object mining, object tagging, feature extraction, classification, SVM

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