Metric learning based object recognition and retrieval

Object recognition and retrieval is an important topic in intelligent robotics and pattern recognition, where an effective recognition engine plays an important role. To achieve a good performance, we propose a metric learning based object recognition algorithm. To represent the invariant object features, including local shape details and global body parts, a novel multi-scale invariant descriptor is proposed. Different types of invariant features are represented in multiple scales, which makes the following metric learning algorithm effective. To reduce the effect of noise and improve the computing efficiency, an adaptive discrete contour evolution method is also proposed to extract the salient feature points of object. The recognition algorithm is explored based on metric learning method and the object features are summarized as histograms inspired from the Bag of Words (BoW). The metric learning methods are employed to learn object features according to their scales. The proposed method is invariant to rotation, scale variation, intra-class variation, articulated deformation and partial occlusion. The recognition process is fast and robust for noise. This method is evaluated on multiple benchmark datasets and the comparable experimental results indicate the effectiveness of our method.