An approach to reduce SIFT computational cost (QSIFT)

This paper describes the method to increase the speed of SIFT feature extraction by feature approximation instead of feature calculation in various layers. SIFT has been proven to be the most robust local rotation and illumination invariant feature descriptor. Additionally, it supports affine transformation. Being fully scale invariant is the most important advantage of this descriptor. The most major SIFT'sdrawback is time-consuming which prevents utilizing SIFTin real time applications. This research attempts to decrease computational cost withoutsacrificing performance. The recent researches in this area approved that direct feature computation is more expensive than extrapolation. Consequently, contribution of this research reduces processing time considerablywithout losing accuracy. Keywords-Interest points, Feature detector, Feature descriptor, Feature extraction, Feature matching, natural image statistics, real-time INTRODUCTION(HEADING 1) Feature detection and image matching are two essential steps in machine vision and robotics applications such as object recognition and matching [5], 3D scene reconstruction [6], motion tracking [7], image representation [8], image classification and retrieval [9], robot localization [10], texture classification [11] and biometrics systems [12]. A desirable feature detection methodmust be invariant to image transformations such as scale, illumination, rotation and affine transformations. Scale Invariant Feature Transform (SIFT) is accurate and efficient in many applications speciallyobject detection and recognition [1]. SIFT major drawback in real-time application is computational complexity. Speeded-Up Robust Features (SURF) includes detection, description, and matching steps [4]. However, SURF is faster than SIFT but its accuracy is less. Binary Robust Independent Elementary Features (BRIEF) is another alternative for SIFT.Although theBRIEFcomputational costsless than SIFT but itsmatching rate is not comparable to SIFT in some applications [15]. Another efficient alternative to SIFT or SURF is Oriented FAST and Rotated BRIEF (ORB) [16]. SIFT is the most robust descriptor considering advantages and disadvantages of all mentioned descriptor in this section. The rest of this paper is organized as follows: 2 describes related work,3 includes proposed methodology in this research,4 contains the comparison between the current used dataset in this area and developed datasets by this research and 5 represents experimental evaluation and finally further development and conclusions is available in 6. RELATED WORK Feature channel scaling Feature channel scaling is a method that increases calculation speed feature extraction in down or up sample based on original image without losing a considerable degree of accuracy [2]. This method has studied the image behavior and multi scale's features. And observed since each single image inside a specific dataset includes small patches thus its behavior is similar to the dataset behavior. As it indicates in following formula [2]. f Ω ( I s )≡ 1 hs w s k ∑ ijk C s(i , j , k ) where C s=Ω (I s)(1) Assuming the Ω is any low-level shift invariant function and I is input image then C represents the new channel of image as: C=Ω( I ) The several local and global features can be described by formula (1)[3]. If Is denotes input image (I) at scale s then: hs× w s=s (h ×w ) when h × w denotes dimension of I and hs× w s is dimension of Is. ∀ s>1, Is the higher resolution version of I, while ∀ s<1, Is obtained by interpolation of I. In fact f Ω ( I s ) represented the global mean of Cs. The relation between input image and its scales can be described by (2). f Ω ( I s 1) ∕ f Ω ( I s2 )=(S1/S2) − Ω+ ε (2) If ε denotes the error then the effort has been made to train λ❑which lead E [ ε ] ≈ 0. It is valuable to note that each channel type Ω has own λΩ. All parameters in (2) are available except λΩthat has been learnt in learning phase then uses in (3). Assuming s1=s , s2=1, by rearranging (2) ,(3) is obtained: f Ω ( I s ) ≈ f Ω ( I ) s −λΩ (3) Beside for any corresponding windows ws in Is and w in I, the following 2