Context Based Prior Probability Estimation of Object Appearance

Abstract

This paper presents a method to estimate the prior probability of object appearance and position from only context information. The context is extracted from a whole image by Gabor filters. The conventional method represented the context by mixture of Gaussian distributions. The prior probabilities of object appearance and position were estimated by generative model. However, we define the probability estimation of object appearance as the binary-classification problem whether an input image contains the specific object or not. The Support Vector Machine is used to classify them, and the distance from the hyperplane is transformed to the probability using a sigmoid function. We also define the estimation problem of object position in an image from only the context as the regression problem. The position of object in an image is estimated by Support Vector Regression. Experimental results show that the proposed method outperforms the conventional method.