Feature extraction and measurement algorithm based on color in image database

2020 ◽  
Vol 38 (4) ◽  
pp. 3885-3891
Author(s):  
Zhonghai Nong
Keyword(s):  
Feature Extraction ◽  
Image Database ◽  
Measurement Algorithm

Simulation of Fast Retrieval Method for Large-Scale Image Database

2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Great Increase ◽  
Texture Features ◽  
Image Database ◽  
Image Databases ◽  
Matching Method ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Database Constraints

The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

scholarly journals Interrelationship identification between humans from images using two class classifier

2018 ◽  
Vol 7 (2.21) ◽  
pp. 5 ◽  
Author(s):  
Amit Verma ◽  
T Meenpal ◽  
B Acharya
Keyword(s):  
Feature Extraction ◽  
Feature Detection ◽  
Confusion Matrix ◽  
Image Database ◽  
Identification Algorithm ◽  
Bag Of Words ◽  
Human Beings ◽  
Feature Detectors ◽  
Surf Features

The paper proposes an automatic interrelationship identification algorithm between human beings. The image database contains two interrelationship classes i.e. two people hugging and handshaking each other. The feature detection and feature extraction has been done using bag of words algorithm. SURF features and FAST features are used as feature detectors. Finally, the extracted features have been applied to SVM for classification. We have tested the classifier against a set of test images for both feature detectors.  Finally, the accuracy of the classifier has been calculated and confusion matrix has been plotted.  

Railway bearing and cardan shaft fault diagnosis via an improved morphological filter

2019 ◽  
Vol 19 (5) ◽  
pp. 1471-1486 ◽  
Author(s):  
Yifan Li ◽  
Ming J Zuo ◽  
Zaigang Chen ◽  
Jianhui Lin
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Morphological Filter ◽  
Scale Selection ◽  
Morphological Filters ◽  
Sensitivity Measurement ◽  
Vibration Signals ◽  
Fault Sensitivity ◽  
Measurement Algorithm

Railway faults are usually observed as impulses in the vibration signal, but they are mostly immersed in noise. To effectively remove noise and identify the impulses, an improved morphological filter is proposed in this article. The proposal focuses on two aspects: a novel gradient convolution operator is proposed for feature extraction, and a new fault sensitivity measurement algorithm is proposed for scale selection because a morphological filter’s effectiveness is mainly determined by these two elements. The performance of the improved morphological filter is evaluated with real vibration signals measured from train’s axle bearings and cardan shafts. From the analysis of three sets of railway faults, the results indicate that the proposed morphological filter effectively detects the faults. Compared with three reported morphological filters, the proposed method has better diagnosis effectiveness.

scholarly journals Design of Feature Extraction in Content Based Image Retrieval (CBIR) using Color and Texture

2012 ◽  
pp. 157-161
Author(s):  
Swati V. Sakhare ◽  
Vrushali G. Nasre
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Image Database ◽  
Color Histogram ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Efficient Approach ◽  
Novel Method ◽  
Fuzzy Color Histogram ◽  
Color Texture

Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tammura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background.

Feature Extraction Using Composite Individual Genetic Programming: An Application to Mass Classification

2012 ◽  
Vol 198-199 ◽  
pp. 468-473 ◽  
Author(s):  
Ying Hua Lv ◽  
Yu Ting Guo ◽  
Hui Sun ◽  
Ming Zhang ◽  
Jian Zhong Wang
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Genetic Programming ◽  
Cancer Diagnosis ◽  
Image Database ◽  
Breast Cancer Diagnosis ◽  
Input Feature ◽  
Novel Method ◽  
Mass Classification ◽  
Discriminatory Performance

This paper proposes a novel method for breast cancer diagnosis using the features generated by genetic programming (GP). We developed a new individual combination pattern (Composite individual genetic programming) which regards several individual as one unity to generate more powerful features that can improve the discriminatory performance of a classifier and reducing the input feature dimensionality at the same time. The performance of the proposed method is demonstrated by extensive experiments on MIAS and DDSM mammographic image database.

Visual Feature Extraction under Wavelet Domain for Image Retrieval

2005 ◽  
Vol 277-279 ◽  
pp. 206-211 ◽  
Author(s):  
Won Bae Park ◽  
Eun Ju Ryu ◽  
Young Jun Song ◽  
Jae Hyeong Ahn
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Frequency Band ◽  
Spatial Information ◽  
Image Database ◽  
Wavelet Domain ◽  
Visual Feature ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Visual Feature Extraction

In this paper, we propose a new visual feature extraction method for content-based image retrieval (CBIR) based on wavelet transform which has both spatial-frequency and multi-resolution characteristics. We extract visual features for each frequency band in wavelet transformation and use them for CBIR. The lowest frequency band involves utilizing the spatial information of an original image. We extract 64 feature vectors using fuzzy homogeneity in the wavelet domain, which considers both the wavelet coefficients and the spatial information of each coefficient. In addition, we extract 3 feature vectors using the energy values of high frequency bands, and store those to the image database. As a query, we retrieve the most similar image from the image database according to the 10 largest homograms (normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

scholarly journals Face Recognition based Feature Extraction using Principal Component Analysis (PCA)

2020 ◽  
Vol 3 (2) ◽  
pp. 182-191
Author(s):  
Muhammad Zulfahmi Nasution
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Face Recognition ◽  
Digital Image ◽  
Principal Component ◽  
Image Database ◽  
Component Analysis ◽  
Facial Features ◽  
Semantic Features ◽  
Pca Method

The human face is an entity that has semantic features. Face detection is the first step before face recognition. Face recognition technique is an identification process based on facial features. One feature extraction approach for facial recognition techniques is the Principal Component Analysis (PCA) method. The PCA method is used to simplify facial features and characteristics in order to obtain proportions that are able to represent the characteristics of the original face. The purpose of this research is to construct facial patterns stored in a digital image database. The process of pattern construction and face recognition starts from objects in the form of face images, side detection, pattern construction until it can determine the similarity of face patterns to proceed as face recognition. In this research, a program has been designed to test some samples of face data stored in a digital image database so that it can provide a similarity in the face patterns being observed and its introduction using PCA

Image feature extraction algorithm in big data environment

2020 ◽  
Vol 39 (4) ◽  
pp. 5109-5118
Author(s):  
Yubao Zhang
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Image Matching ◽  
Image Database ◽  
Scale Space ◽  
Image Feature ◽  
Aerial Image ◽  
Image Feature Extraction ◽  
Matching Algorithm ◽  
Improved Algorithm

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.

scholarly journals A NEW DESCRIPTOR FOR IMAGE RETRIEVAL USING CONTOURLET COOCCURRENCE

2012 ◽  
Vol 15 (2) ◽  
pp. 5-16 ◽  
Author(s):  
Hoang Duc Nguyen ◽  
Thuong Tien Le ◽  
Tuan Hong Do ◽  
Cao Thu Bui
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Image Retrieval ◽  
Image Database ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Grey Level ◽  
Slight Improvement ◽  
Retrieval Effectiveness ◽  
Occurrence Matrix

In this paper, a new descriptor for the feature extraction of images in the image database is presented. The new descriptor called Contourlet Co-Occurrence is based on a combination of contourlet transform and Grey Level Co-occurrence Matrix (GLCM). In order to evaluate the proposed descriptor, we perform the comparative analysis of existing methods such as Contourlet [2], GLCM [14] descriptors with Contourlet Co-Occurrence descriptor for image retrieval. Experimental results demonstrate that the proposed method shows a slight improvement in the retrieval effectiveness.

Sign in / Sign up

Export Citation Format

Share Document