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...

In this paper we describe our experiments in the automatic search task of TRECVid 2007. For this we have implemented a new video search technique based on SIFT features and manual annotation. We submitted two runs, one solely based on the SIFT features with keyframe matching and the other based on adapted SIFT features for video retrieval in addition to manually annotated data.

For visual word based location recognition in 3D models we propose a novel distance-weighted scoring scheme. Matching visual words are not treated as perfect matches anymore but are weighted with the distance of the original SIFT feature vectors before quantization. To maintain the scalability and efficiency of vocabulary tree based approaches PCA compressed SIFT feature vectors are used instea...

This paper investigates a face recognition system based on Scale Invariant Feature Transform (SIFT) feature and its distribution on feature space. The system takes advantage of SIFT which possess strong robustness to expression, accessory pose and illumination variations. Since we use each of SIFT keypoint as the feature of face and SIFT keypoints are very complicated in feature space, we apply...

This paper summarizes the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF). This paper uses KNN (K-Nearest Neighbor) and Random Sample Consensus (RANSAC) to the three methods in order to analyze the results of the methods’ application in recognition. KNN is used to find the matches, an...

According to SIFT algorithm does not have the property of affine invariance, and the high complexity of time and space, it is difficult to apply to real-time image processing for batch image sequence, so an improved SIFT feature extraction algorithm was proposed in this paper. Firstly, the MSER algorithm detected the maximally stable extremely regions instead of the DOG operator detected extrem...