Abstract
In this paper, we present a new method to recognize object class based on local appearance features and context information. At first, local descriptors of object class appearance are clustered, then part classifiers are trained to select the most distinctive image patches and visual context information around them are extracted to keep the robustness to object occlusion and background clutter. Finally general probabilistic models are built to implement image classification by integrating the context information with local scale-invariant appearance characteristics. Compared with previous work, we obtain a better classification with limited and unnormalized training samples. Experiment results show that the proposed method can outperform other previous methods even under large scale object classes, therefore the significance of appearance-based discriminative part classifiers is demonstrated and confirmed.
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