Speeding up the Detection of Scale-Space Extrema in SIFT Based on the Complex First Order System

For object recognition based on nearest neighbor search of local descriptors such as SIFT, it is important to minimize the cost of extracting image features. The processes of extracting image features are composed of scale-space extrema detection, keypoint localization, orientation assignment and keypoint descriptor.In this paper, we propose a new method of efficient scale-space extrema detection. The scale space of an image is defined as a function, that is produced from the convolution of a variable-scale Gaussian with an input image. Number in which multiplication is executed is reduced by complex first order system proposed by Aoshima. From experimental results with computation time and recognition of image with ANN, we have confirmed that the proposed method is capable of achieving a recognition rate as same as the original method, and 1/3 of the computation time with the original method.