SIFT (Scale Invariant Feature Transform) points are scale-space extreme points, representing local minutiae structure features in the Gaussian scale space. SIFT intensity, as a novel no-reference metric, is feasible to assess various common distortions without the access to reference images. The metric introduces image preprocessing: neighborhood enhancement through contrast enhancement of adja...

Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the fact that scaleinvariant feature transform (SIFT) has be...

This article presents a fully functional GPU-based implementation of Scale Invariant Feature Transform (SIFT) algorithm. SIFT is a popular image feature extraction algorithm. Although it is a powerful algorithm for image matching but it is also computationally very expensive. This makes it difficult to use especially in real time applications. We purpose to expedite SIFT through GPU-based imple...

Face recognition has been widely investigated in the last decade. However, real world application for face recognition is still a challenge. Most of these face recognition algorithms are under controlled settings, such as limited viewpoint and illumination changes. In this paper, we focus on face recognition which tolerates large viewpoint change. A novel framework named Lucas-Kanade Scale Inva...

This paper describes the motivations, methods, and automation architecture of a framework for multisource Semantic Information extraction & Fusion for collaborative Threat assessment (SIFT). First, the technical and pragmatic challenges that motivate the research ideas are summarized. Next, a characterization of the activities for generating decision enabling information from multi-source data ...

This paper presents a comparative approach for Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT) algorithm and Principal Component Analysis (PCA) for color images. The motivation to use SIFT algorithm for CBIR is due to the fact that SIFT is invariant to scale, rotation and translation as well as partially invariant to affine distortion and illumination changes...

With explosive growth of multimedia data on internet, the effective information retrieval from a large scale of multimedia data becomes more and more important. To retrieve these multimedia data automatically, some features in them must be extracted. Hence, image feature extraction algorithms have been a fundamental component of multimedia retrieval. Among these algorithms, Scale Invariant Feat...

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these ...

As multimedia data sharing increases, security in mobile devices and its mechanism can be seen as critical. Biometrics combines the physiological behavioral qualities of an individual to validate their character real-time. Humans incorporate attributes like a fingerprint, face, iris, palm print, finger knuckle Deoxyribonucleic Acid (DNA), walk, voice, mark, or keystroke. The main goal this pape...