In this paper, author propose a image recognize system for determinate whether an object is in the query image. The system can be break into three parts: representation, learning, and recognition. Representation means break image into features, and used these features to represent an object. An object is modeled as a constellation parts, where appearance, shape, relative scale and occlusion are all considered by the model. EM algorithm is used as an unsupervised learning method in this paper. And recognition is done by compare the probability ratio of query image having object present to a threshold. The result of this system is significantly better than the baseline. But the scale of the dataset they used is maybe not large enough, one of the dataset only contain couple hundred images. Also they aligned the images make them facing the same way before learning.
This paper provide a complete probability model to model object in a image. It is kind of hard to digest at the first glance. The model consider several parts including scales, but not rotation. Therefore they need to preprocessing the image before learning. The author suggest to improve the system by adding rotation invariance in the future works.
