A relevance feedback algorithm based on the clustering and Parzen window

Hyung Il Koo, Nam Ik Cho

Abstract: A relevance feedback algorithm based on the nonparametric approach is proposed. In the feature space, the algorithm generates multiple hyper-spheres around the regions where the images relevant with the query are densely populated, whereas the conventional algorithm searches the images in a single hyper-ellipsoid region. Then the Parzen window approach is applied to estimate the probability of relevance of each image in these multiple clusters (hyper-spheres). As a result, the relevance region in the feature space expands rapidly and covers arbitrarily shaped spaces with a small number of parameters. Also, since the user needs to determine only the positive images not the ambiguous negative ones, it is more convenient to use compared to some of the existing algorithms requiring negative feedback.