Identification of Image-spam Based on Perimetric Complexity Analysis and SIFT Image Matching Algorithm

摘要

In this paper, a new method is proposed for effective identification of image spam using simplified Scale Invariant Feature Transform (SIFT) algorithm and "Perimetric Complexity" analysis which is composed of double filter layers. Our early work has reduced the time cost of detecting a image-spam by reducing the feature vectors' dimensions of SIFT algorithm from 128 to 40, and to further improve efficiency, "Perimetric Complexity" analysis is added as the first layer to recognize image-spam by detecting the presence of two noise resulting in character breaking or merging. The simplified SIFT algorithm is used in the rest images after the "Perimetric Complexity" analysis. The results of experiment demonstrates that it improves the performance of simplified SIFT algorithm for its high accuracy and rapid speed in detection.