Content-Based Object Movie Retrieval by Use of Relevance Feedback

Abstract

Object movie refers to a set of images captured from different perspectives around a 3D object. Object movie is a good representation of a physical object because it can provide 3D interactive viewing effect, but does not require 3D reconstruction. In order to retrieve the desired object movie from the database, we first map an object movie into a manifold in the feature space. Two different sets of feature descriptors, one dense and one condensed, are designed to sample the manifold. Based on these descriptors, we define the dissimilarity measure between the query and the target in the object movie database. The query we considered can be either a complete object movie or simply a subset of views. In this paper, we further propose a relevance feedback approach to improving retrieved results. Some experimental results are shown to show the potential of our approach.