A Comparative Study of Local Image Feature Description

In view of the local image feature description algorithm of recent year, we compare them with SIFT, which is a classic algorithm. We found the gap of every kind of description algorithm with some image transformations, and this can be a reference when feature description algorithm used in some application of computer vision. Our results show that MROGH and MRRID has the best performance, and then is LIOP, DAISY and HRI-CSLTP are better than SIFT in some image transformation. Introduction Image local invariant feature refers to the image features of the image is invariant to image rotation, scale transformation, affine transformation, perspective transformation, image blur, image compression etc. Because of its excellent properties have been widely used in various applications of computer vision and pattern recognition, such as target recognition [1], wide baseline matching [2], image retrieval [3], panoramic image mosaic [4]. Image local feature description is an important stage of local feature extraction, it refers to the image feature area to detect the feature description into regional description vector, so the more convenient for the following use The simplest feature description method is to transform the pixels in the feature region directly into the description vector, but it is not widely used because of its poor performance. A description of local image features, which most attention is put forward by Lowe SIFT (Scale invariant feature transform) [1]. SIFT algorithm because of its superiority has been widely used, and many scholars have made improvements, such as PCA-SIFT[5], GLOH[6], SURF[7] etc. In recent years, there are many new characteristics description methods have emerged, such as the CS-LBP[8] center symmetric local binary value model LBP (local binary pattern structure descriptors, HRI-CSLTP[9] will CS-LBP expansion into three value coding, the descriptor has a stronger ability to distinguish. DAISY[10] is similar to SIFT and GLOH descriptors, but the calculation is faster than SIFT, and effectively used in wide baseline stereo matching. According to the SIFT algorithm is required to estimate the direction of the main shortcomings, Bing fan, Wang Zhenhua etc. [11-13] studied a new descriptor design framework, it does not need to estimate the main direction, using this framework MROGH (Multisupport region Order-Based gradient histogram) MRRID (Multisupport Region Rotation and Intensity Monotonic Invariant Descriptor) and LIOP (Local Intensity Order Pattern) algorithm is proposed. Based on several classic good performance description algorithm are compared and analyzed. By comparing the performance gap between the various descriptors, when these feature description algorithm was applied to various applications in computer vision can be used as reference. Local Image Feature Image local invariant feature extraction includes two steps: feature detection and feature description Feature detection is to detect the characteristics of location, characteristics of general feature detection as feature points or feature regions Feature description is the representation of the feature. There are many classic algorithms, the classical algorithm reference [14] and [15] respectively for feature detection and feature description are discussed and compared. [15] The new algorithm does not contain some of the good performance in recent years, so this paper a novel algorithm for recent 6th International Conference on Sensor Network and Computer Engineering (ICSNCE 2016) © 2016. The authors Published by Atlantis Press 342 comparative study, including: DAISY, HRI-CSLTP, LIOP, MROGH and MRRID, these five kinds algorithm and SIFT algorithm were compared. Local Image Feature Description Algorithm DAISY Algorithm. DAISY [10] descriptors are Gaussian kernel convolution and weighted based on obtained. For an image, the image is first calculated in each point of the H gradient direction, indicated ) , ( v u Go by the dot ) , ( v u in the direction o of the gradient, the gradient map is expressed as:     ) ( o I Go (1) where I represents the image,  ) ( represents ) 0 , max( ) ( a a   . Then the gradient map and  different values of the Gauss kernel convolution, so as to realize the multi-scale space, we obtain different scale convolution direction map:        ) ( o I G Go (2) For a point in the image gradient direction histogram: T 2 1 ] ), , ( , ), , ( ), , ( [ ) , ( v u G v u G v u G v u h H       (3) where  1 G ,  2 G ,. ,  H G represents  In different directions of the gradient map, then the normalized processing is obtained ) , ( ~ v u h . If the number of different circles Q , DAISY is the descriptor: ] )), , , ( ( ~ , )), , , ( ( ~ )), , , ( ( ~ , )), , , ( ( ~ )), , , ( ( ~ , )), , , ( ( ~ ), , ( ~ [ ) , ( 0 0 T 0 0 1 T 2 0 0 T 2 0 0 1 T 1 0 0 T 1 0 0 1 T 0 0 T 0 0