Scale-invariant feature transform (SIFT) was an algorithm in computer vision to detect and describe local features in images. Due to its excellent performance, SIFT was widely used in many applications, but the implementation of SIFT was complicated and time-consuming. To solve this problem, this paper presented a novel acceleration algorithm for SIFT implementation based on Compute Unified Dev...

This paper first presents a new oRGB-SIFT descriptor, and then integrates it with other color SIFT features to produce the novel Color SIFT Fusion (CSF) and the Color Grayscale SIFT Fusion (CGSF) descriptors for image classification with special applications to biometrics. Classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Ne...

The large-scale video data on the web contain a lot of semantics, which are an important part of semantic web. Video descriptors can usually represent somewhat the semantics. Thus, they play a very important role in web multimedia content analysis, such as Scale-invariant feature transform (SIFT) feature. In this paper, we proposed a new video descriptor, called a temporalcompress and shorter S...

Feature-based image matching is one of the most fundamental issues in computer vision tasks. As the number of features increases, the matching process rapidly becomes a bottleneck. This paper presents a novel method to speed up SIFT feature matching. The main idea is to extend SIFT feature by a few pairwise independent angles, which are invariant to rotation, scale and illumination changes. Dur...

This paper describes our contribution to instance search (INS) task for TRECVID 2012. We present four approaches for this task, (i) histograms of SIFT features as feature vectors and Bhatacharya distance for similarity detection (ii) feature vector is combination of SIFT features alone, while for matching we used a basic descriptor matching algorithm (iii) IR based approach using SIFT features ...