Learning Generative Models of Object Parts from a Few Positive Examples

A number of computer vision problems such as object detection, pose estimation, and face recognition utilise local parts to represent objects, which include the distinguished information of objects. In this work, we introduce a novel probabilistic framework which automatically learns class-specific object parts (landmarks) in generative-learning manner. Encouraged by the success in learning and detecting facial landmarks, we employ bio-inspired multi-resolution Gabor features in the proposed framework. Specifically, complex-valued Gabor filter responses are first transformed to landmark specific likelihoods using Gaussian Mixture Models (GMM), and then efficient response matrix shift operations provide detection over orientations and scales. We avoid the undesirable characteristic of generative learning, a large number of training instances, with the novel concept of randomised Gaussian mixture model. Extensive experiments with public benchmarking Caltech-101 and BioID datasets demonstrate the effectiveness of our proposed method for localising object landmarks.