Adapting k-d Trees to Visual Retrieval

The most frequently occurring problem in image retrieval is nd the similar image which in general is nding the nearest neighbor From the literature it is well known that k d trees are e cient methods of nding nearest neighbors in high dimensional spaces In this paper we survey the relevant k d tree literature and adapt the most promising solution to the problem of image retrieval by nding the best parameters for the bucket size and threshold We also test the system on the Corel Studio photo database of images and measure the user response times and retrieval accuracy