Cat face detection with two heterogeneous features

In this paper, we propose a generic and efficient object detection framework based on two heterogeneous features and demonstrate effectiveness of our method for a cat face detection problem. Simple Haar-like features with AdaBoost are fast to compute but they are not discriminative enough to deal with complicated shape and texture. Therefore, we cascade joint Haar-like features with AdaBoost and CoHOG descriptors with a linear classifier. Since the CoHOG descriptors are extremely high dimensional pattern descriptors based on gradient orientations, they have a strong classification capability to represent various cat face patterns. The combination of these two distinct classifiers enables fast and accurate cat face detection. The experimental result with about 10,000 cat images shows that our method gives better performance in comparison with the state-of-the-art cat head detection method, although our method does not exploit any cat specific characteristics.