Improve the Image Feature-Matching Based on Scale Invariant Feature Transform Algorithm

2012 ◽  
Vol 6 (1) ◽  
pp. 770-773
Author(s):  
Wenyu Chen ◽  
Yanli Zhao
Keyword(s):  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

scholarly journals Annular Spatial Pyramid Mapping and Feature Fusion-Based Image Coding Representation and Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mengxi Xu ◽  
Yingshu Lu ◽  
Xiaobin Wu
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Image Feature ◽  
Classification Model ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Spatial Pyramid

Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.

scholarly journals A non-target structural displacement measurement method using advanced feature matching strategy

2019 ◽  
Vol 22 (16) ◽  
pp. 3461-3472 ◽  
Author(s):  
Chuan-Zhi Dong ◽  
F Necati Catbas
Keyword(s):  
Measurement Method ◽  
Feature Matching ◽  
Displacement Measurement ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Matching Strategy ◽  
Scale Invariant Feature ◽  
Visual Geometry

Most of the existing vision-based displacement measurement methods require manual speckles or targets to improve the measurement performance in non-stationary imagery environments. To minimize the use of manual speckles and targets, feature points regarded as virtual markers can be utilized for non-target measurement. In this study, an advanced feature matching strategy is presented, which replaces the handcrafted descriptors with learned descriptors called Visual Geometry Group, of the University of Oxford descriptors to achieve better performance. The feasibility and performance of the proposed method is verified by comparative studies with a laboratory experiment on a two-span bridge model and then with a field application on a railway bridge. The proposed approach of integrated use of Scale Invariant Feature Transform and Visual Geometry Group improved the measurement accuracy by about 24% when compared with the commonly used existing feature matching-based displacement measurement method using Scale Invariant Feature Transform feature and descriptor.

scholarly journals Bidirectional scale-invariant feature transform feature matching algorithms based on priority k-d tree search

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141668270
Author(s):  
XiangShao Liu ◽  
Shangbo Zhou ◽  
Hua Li ◽  
Kun Li
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Scale Invariant Feature Transform ◽  
Tree Search ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Key Steps ◽  
Scale Invariant Feature

In this article, a bidirectional feature matching algorithm and two extended algorithms based on the priority k-d tree search are presented for the image registration using scale-invariant feature transform features. When matching precision of image registration is below 50%, the discarding wrong match performance of many robust fitting methods like Random Sample Consensus (RANSAC) is poor. Therefore, improving matching precision is a significant work. Generally, a feature matching algorithm is used once in the image registration system. We propose a bidirectional algorithm that utilizes the priority k-d tree search twice to improve matching precision. There are two key steps in the bidirectional algorithm. According to the case of adopting the ratio restriction of distances in the two key steps, we further propose two extended bidirectional algorithms. Experiments demonstrate that there are some special properties of these three bidirectional algorithms, and the two extended algorithms can achieve higher precisions than previous feature matching algorithms.

A Comparison of Two Typical Local Feature Matching Algorithm: SIFT and MSER

2014 ◽  
Vol 687-691 ◽  
pp. 4119-4122
Author(s):  
Xiao Cun Jiang ◽  
Xiao Liu ◽  
Tao Tang ◽  
Xiao Hu Fan ◽  
Xiao Cui
Keyword(s):  
Feature Matching ◽  
Local Feature ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Distance Difference ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Shooting Distance

Scale invariant feature transform matching algorithm and Maximally Stable Extremal Regions matching algorithm have been widely used because of their good performance. The two local feature matching algorithms were compared through numbers of experiments in this paper. The experiment results showed that SIFT is good at dealing with the image distortion from shooting distance difference and small shooting viewpoint deviation; MSER is good at handling the complicated affine distortion from big shooting viewpoint deviation. From the aspect of scene types, the performance of SIFT is good both to structure images and texture images. MSER is suitable for the matching of structure images, but not so successful to that of texture images.

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