Study On Method Of Image Local Feature Description

It’s very complicated for computer when it comes to the ability of perception andrecognition, even it’s a very simple object to be recognized. The most difficult point forcomputer recognition is how to express the object. It needs the ability to distinguish oneobject from another no matter its different size, different view and different position. Featureselection is the key process in computer vision which can greatly affects the results. Duringthe past decade, the progresses of local feature prompt computer vision research. With thehelp of multi-scale analysis technology and statistics technology, people draw various kind oflocal image feature from each block of image which has a better express of image. It’s widelyused in the area of object recognition, registration, image stitch and robot vision etc.We have a deep research on various local image features. A comparison study hasdeveloped on Harris, SIFT, SURF and MSER. SIFT algorithm is selected as the start point forits good effect. We put forward some improvement according the shortcomings. What’s more,the improved feature is used on scene image classification and the experiments demonstrateits good effect on image classification. The details and the innovation are as follows:1. The scale-invariant feature transform algorithm proposed by Lowe has a low efficiencyand restricts its application. The algorithm based on rounded projection proposed in our paperapplies Fast Fourier Transform algorithm (FFT) on the projection function to compute thefirst harmonic components which are used to pre-screen the feature points that extracted bySIFT algorithm. After the pre-screening, we get the descriptors according to the local areafeatures of left points. The experiments shows that it has a smaller number of feature pointsthan the original SIFT algorithm, so it improves the efficiency and has a better performance.2. The model of word bags use the SIFT descriptors to formulate the image vision word bycluster method. SIFT algorithm is a detector of blob region of image by LOG kernel function.Instead, we substitute the SIFT detector for Fan-SIFT algorithm. Fan-SIFT not only detectsthe blob region in image, but also the fan region. Accordingly, we use a feature descriptor offan shapes. Fan-SIFT can find different kinds of blob region in image and form the descriptorswith a smaller dimension. Experiments are processed on the data set of13scene images anddata set of Caltech101. The results show a better effect on image classification.We also process the comparison experiments on the blob image features which focus on the match results of different scale, different size and different position, analyze the repeatabilityof different feature detectors. We give an intuitive description on the quality of blob imagefeatures.