Cat Face Detection Based On Candidategeneration

In the field of object recognition, researchers have achieved good results.The technology of object recognition has been applied to all walks of life, such as in the biological field, military areas, agriculture and so on. Recently, people pay more attention to the animals detection. Inspired by the success of face detection, we consider apply its technology to the animal detection.This paper does works around cat face detection.We adopts the thought of coarse to fine to detect cat face, which include two steps, the first to select candidate rectangles that most likely contain the cat face, the second to apply the deformable parts model to the former results to find the accurate position.The first job utilize the rich color and texture information of cat. The color feature is one of the most common low-level features and cat has rich color information. In order to make the objectness method generate candidate bounding boxes to favor cats more, we apply the Gaussian Mixture Model to the segmentation of the color space, after conversion of the image from RGB to the HSV color space. the experiment results show that the position of the cat in the image can be divided roughly. Based on the results, we run objectness method to generate bounding box. With the joined priori information, we can get more accurate candidate areas while considering fewer bounding boxes.As the most common image features in computer vision, texture can be represented effectively using LBP. In order to make full use of the rich texture information and obtain the local feature information, we apply the Spatial Pyramid Model on the extracted LBP feature to get the spatial information of the feature.Meanwhile, based on the idea of Haar-like feature, we apply the rectangle model on the SPM to capture the variations of features of different regions in the image. We conduct the experiments on the processed Microsoft Cat Dataset and the results validate the effectiveness of the proposed method. After extracting features of the dataset,, we train the cascaded Adaboost classifier and then get the candidate regions.Combining the two results in the first step, we obtain the position where the cat locate roughly. In order to accurately detect object in the second step, we train the deformable part model of cat face. Then we apply the DPM to detect cat face on the candidate rectangles. We conduct the experiment on the Microsoft Cat Dataset and get a better detection rate about cat face than other methods. And the results prove that it’s better to detect cat face on the local area than on the whole image.