| Content-based image retrieval involves the process of querying and retrieving images from an image database using the image feature descriptors rather than its corresponding text annotations. Different descriptors such as MSER and DAISY have been shown to have different strengths in identifying images. We study some of these descriptors and using SURF and DAISY, we show that a combination of such descriptors perform better image recognition and classification than individual descriptors. Also, we concentrate on studying data structures that are most suitable for our model. Focusing on the approximate nearest neighbor query problem, we implement the "Location Sensitive Hashing" (LSH) technique and the approximate kd-tree technique over SURF and DAISY descriptors combination and try to improve our query response time with very little loss in accuracy. We then show how our robust model can be deployed on parallel and distributed environments, for batch processing of image queries. |
