SIFT Feature with Relevance Feedback for Image Retrieval Written by Administrator Wednesday, 16 March 2011 08:52 Last Updated Monday, 21 March 2011 07:32 In this paper, we used the Scale Invariant Feature Transform (SIFT) feature for image retrieval. SIFT descriptors are invariant to image scaling, transformation,rotation and partially invariant to illumination changes and affine, gives the loc...

SIFT is a novel and promising method for iris recognition. However, some shortages exist in many related methods, such as difficulty of feature extraction, feature loss, and noise point introduction. In this paper, a new method named SIFT-based iris recognition with normalization and enhancement is proposed for achieving better performance. In Comparison with other SIFT-based iris recognition a...

Recent years have encountered a massive growth in social networking due to which immense numbers of videos are being shared on video sharing sites but issue of copyright infringement arises with uploading of illicit or transformed versions of original videos. Thus safeguarding copyrights of digital media has become matter of concern. In this paper we propose a video copy detection system which ...

Fast and robust images matching are of vital importance in low-altitude photogrammetric process. Among the most popular features of images matching are currently SIFT and Harris etc. For time-critical applications such as disaster monitoring, the SIFT features extraction are too slow, and the location accuracy of SIFT and Harris is insufficient in photogrammetric process. In this paper, we pres...

This paper introduces D-SIFT, a Web-based browser application that provides untrained users in Formal Concept Analysis with practical and intuitive access to core analysis functionality in Formal Concept Analysis. D-SIFT is an information systems architecture that supports natural search processes over a predefined database schema and its attribute values. This enables the user to build concept...

Image matching using feature extraction is an important issue in computer vision tasks. The main drawback of matching process is the bottleneck problem that rapidly appeared when the number of features increased. This paper produced an adaptive approach to improve Scale Invariant Feature Transform (SIFT) matching. The main idea is to increase the number of SIFT points by using Adaptive PCA in w...

This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region...

SIFT Feature with Relevance Feedback for Image Retrieval Written by Administrator Wednesday, 16 March 2011 08:52 Last Updated Monday, 21 March 2011 07:32 In this paper, we used the Scale Invariant Feature Transform (SIFT) feature for image retrieval. SIFT descriptors are invariant to image scaling, transformation,rotation and partially invariant to illumination changes and affine, gives the loc...

Recently, Scale Invariant Feature Transform (SIFT) algorithm is widely used in feature extraction and image matching. However, it has some defects, such as large volume of computational data and low efficiency of image matching. To address these defects, adaptive feature extraction and image matching based on Haar Wavelet Transform and SIFT (AHWT-SIFT) is proposed in this paper. In view of the ...