Meta-Classifier Based on Boosted Approach for Object Class Recognition

Abstract

Object class recognition deals with the classification of individual objects to a certain class. In images of natural scenes, objects appear in a variety of poses and scales, with or without occlusion. Object class recognition typically involves the extraction, processing and analysis of visual features such as color, shape, or texture from an object, and then associating a class label to it. In this study, global shape and local features are considered as discriminative features for object class recognition. Both local and shape features are combined in order to obtain better classification performance for each object class. A meta-classifier framework is proposed as a model for object class recognition. Meta-classifier is used to learn a decision classifier that optimally predicts the correctness of classification of base classifier for each object. In this framework, base classifiers based on boosting approach are trained using the local and global shape features, respectively. Then, these classifiers results are combined as input to the meta-classifier. The results from classification experiments showed that meta-classifier based on boosted approach performs better compared to some state-of- the-art approaches in object class recognition.