Object Recognition with Task Relevant Combined Local Features

Abstract

A number of cortex-like hierarchical models of object recognition have been proposed these years. In this paper, we improve them by introducing supervision during forming combined local features. The traditional cortex-like hierarchical models always contain three layers which imitate the functions of neurons in ventral visual stream of primates. The bottom layer detects orientation information in a local area. Then the middle layer combines these information to form combined features. Finally, the top layer integrates combined features to form global features which are input into a classifier. In these models, three stages to form global features are all unsupervised. The supervision procedure only occurs after global features are generated, which is implemented by the classifier. But we think the supervision should occurs earlier. For a particular object recognition task, the second stage of generating global features is also supervised because only task relevant combinations are useful. In our paper, we analyze why introducing supervision in this stage is necessary. And we explain task relevant combined local features can be extracted by some feature selection algorithms. We also apply this improved system to a series of object classification problems and compare it with traditional models. The simulation results show that our improvement really boosts object recognition performance.