Object Recognition And Classification Based On Local Image Features

Object recognition and classification is the hot issue in the fields of optical, computer vision and artificial intelligence etc.. It consists of object representation and classifier design. In recent years, the research of local image features developed fast. This paper presents object classification and recognition based on local image features.This dissertation studies local image features, a well designed local image features should possess three typical characteristics:repeatable, fast and invariant descriptor to image transformations. We surveyed and compared three popular and widely used local image features using novel evaluation metrics we presented.In this paper, we use neighborhood pixel characteristics, including HSV color space, Gaussian-weighted gradient magnitudes and orientations, sampled in specific window around interest point to enhance the description. Experimental results show that, the performance of EPD, in the distinctiveness and invariance aspects, is as good as SIFT, while the time cost of descriptor construction and matching is far less than it.Moreover, the EPD combines more image characteristics, which makes it be able to describe common image points, but not limited to the image extreme points. These advantages make the EPD finding new applications in the field of dense stereo matching, and it shows good match results.The application of local image features can also be extended to forged seal identification. The difficult problem of forged seal identification is image registration. We proposed the new system of seal identification, it can recognition by the consistency ratio of feature line randomly generated. The results show that this method is applicable to all types and content seals.The Research in object recognition, after extracting the local image feature, we can use BoW to cluster of these feature vectors in the form of histograms, then use support vector machine as classifier. The goal is object recognition and classification.