scholarly journals Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing

2021 ◽  
Vol 38 (1) ◽  
pp. 181-190
Author(s):  
Zhenfeng Wei ◽  
Xiaohua Zhang
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Metric Learning ◽  
Image Feature ◽  
Retrieval Method ◽  
Business Application ◽  
Product Image ◽  
Product Image Retrieval ◽  
High Level ◽  
Key Techniques

The new retail is an industry featured by online ecommerce. One of the key techniques of the industry is the product identification based on image processing. This technique has an important business application value, because it is capable of improving the retrieval efficiency of products and the level of information supervision. To acquire high-level semantics of images and enhance the retrieval effect of products, this paper explores the feature extraction and retrieval of ecommerce product images based on image processing. The improved Fourier descriptor was innovatively into a metric learning-based product image feature extraction network, and the attention mechanism was introduced to realize accurate retrieval of product images. Firstly, the authors detailed how to acquire the product contour and the axis with minimum moment of inertia, and then extracted the shape feature of products. Next, a feature extraction network was established based on the metric learning supervision, which is capable of obtaining distinctive feature, and thus realized the extraction of distinctive and classification features of products. Finally, the authors expounded on the product image retrieval method based on cluster attention neural network. The effectiveness of our method was confirmed through experiments. The research results provide a reference for feature extraction and retrieval in other fields of image processing.

scholarly journals Design and Implementation of a Student Archive Retrieval Method Based on Image Processing

2020 ◽  
Vol 34 (4) ◽  
pp. 487-494
Author(s):  
Lei An ◽  
Aihua Li
Keyword(s):  
Image Processing ◽  
Information Management ◽  
Image Feature ◽  
Similarity Measurement ◽  
Function Learning ◽  
Retrieval Method ◽  
Design And Implementation ◽  
Network Modules ◽  
Novel Method ◽  
Effectiveness And Efficiency

Compared with traditional manual archive organization and review, the student archive management system can manage massive student archives in a refined, regular, and scientific manner. The effectiveness and efficiency of the retrieval method directly bears on the utilization effect of student archives. Based on image processing, this paper puts forward a novel method for student archive retrieval, which greatly improves the classification, recognition, and information management of images in student archives during the retrieval. Firstly, a framework of student archive retrieval was introduced based on image processing. Next, a deep convolutional neural network (DCNN) was constructed for hash learning, and the functions of the three network modules were detailed, including image feature extraction, hash function learning, and similarity measurement. Finally, several indices were selected to evaluate the retrieval effect of student archives. The proposed method was proved effective and feasible through contrastive experiments. The research results provide a theoretical reference for the application of our method in other fields of image retrieval.

scholarly journals Detection of Glaucoma using ORB (Oriented FAST and Rotated BRIEF) Feature Extraction

2021 ◽  
pp. 153-158
Author(s):  
Kazi Safayet Md. Shabbir ◽  
Md. Imteaz Ahmed ◽  
Marzan Alam
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Texture Analysis ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Fundus Camera ◽  
Analysis Methods ◽  
Binary Descriptor ◽  
Descriptor Approach ◽  
Precision And Accuracy

This research was utilized to identify glaucoma, a type of eye illness. This endeavor necessitates the use of pictures from the fundus camera for image processing. This study reflects the effort done to detect glaucoma-affected eyes utilizing image feature extraction using Oriented FAST and Rotated BRIEF (ORB). ORB is a binary descriptor approach that is based on BRIEF and is highly fast. This technique is insensitive to picture noise and is invariant to any rotation. ORB is two orders of magnitude faster than SURF and performs similarly to SIFT. It is more efficient than other texture analysis methods. It is less computationally difficult than other approaches in the literature. This technique extracts features and detects texture by inspecting each pixel of the retina picture. It was trained on 160 fundus pictures of normal and glaucoma-affected retinas. After that, any healthy or glaucoma-affected eye may be easily recognized by obtaining an accurate eye picture. The results reveal that this technique has a precision and accuracy of more than 90%.

Radial Moments for Image Retrieval

2017 ◽  
pp. 358-389
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

The Imapct of Low-Level Features in Semantic-Based Image

2011 ◽  
pp. 23-45 ◽  
Author(s):  
Konstantinos Konstantinidis ◽  
Antonios Gasteratos ◽  
Ioannis Andreadis
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Experimental Results ◽  
Image Feature ◽  
Statistical Tool ◽  
Image Feature Extraction ◽  
Low Level ◽  
High Level

Image Retrieval (IR) is generally known as a collection of techniques for retrieving images on the basis of features, either low-level (Content-based IR) or high-level (Semantic-based IR). Since Semantic-based features rely on low-level ones, in this chapter the reader is initially familiarized with the most widely used low-level features. An efficient way to present these features is by means of a statistical tool capable of bearing concrete information, such as the histogram. For use in IR, the histograms extracted from the previously mentioned features need to be compared by means of a metric. The most popular methods and distances are, thus, apposed. Finally, a number of IR systems using histograms are presented in a thorough manner and their experimental results are discussed. The steps in order to develop a custom IR system, along with modern techniques in image feature extraction are also presented.

scholarly journals Slope Collapse Detection Based on Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-Dai An ◽  
Xiang-Wen Xie ◽  
Di Wu ◽  
Ke-Feng Song
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Recognition ◽  
Motion Detection ◽  
Detection Algorithm ◽  
Image Feature ◽  
Moving Targets ◽  
Image Feature Extraction ◽  
Slope Collapse ◽  
River Embankment

In this paper, we study a task of slope collapse detection (SCD) for river embankment and formulate it as the tasks of motion detection and image recognition. Specifically, we introduce an SCD method based on motion detection and image recognition technologies to help inspector attendants detect the slope collapse. In this method, we use the foreground motion detection algorithm to identify the slope collapse of the scene of the river embankment. Since the moving targets in the foreground may not only be the slope collapse but also maybe some biology, we further use the image feature extraction and image recognition technology to recognize the foreground motion area, thus eliminating the influence of the biology on the detection results. Experimental results on the relevant scene data show that the proposed method can identify the slope collapse in real-time, and can effectively eliminate the motion interference of the biology, which has a high practical value.

scholarly journals Review of local binary pattern operators in image feature extraction

2020 ◽  
Vol 19 (1) ◽  
pp. 23
Author(s):  
Shihab Hamad Khaleefah ◽  
Salama A. Mostafa ◽  
Aida Mustapha ◽  
Mohammad Faidzul Nasrudin
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Local Binary Pattern ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Classification Image ◽  
Current Trends ◽  
Segmentation Pattern ◽  
Information Image

<span>With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing.</span>

Radial Moments for Image Retrieval

2018 ◽  
pp. 2420-2451
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

scholarly journals A Study on Various Image Feature Extraction Techniques

2019 ◽  
pp. 151-157
Author(s):  
Merllin Ann George ◽  
L. C. Manikandan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Character Recognition ◽  
Quantitative Information ◽  
Image Feature ◽  
Extraction Techniques ◽  
Unique Form ◽  
Image Feature Extraction ◽  
Time Consumption ◽  
Matrix Vector

Feature Extraction is the technique of extracting quantitative information from a image. Feature plays a very important role in the area of image processing. The purpose of feature extraction technique in image processing is to represent the image in its compact and unique form of single values or matrix vector. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. The aim of this paper is to give the overview of image feature extraction techniques for young learners and researchers.

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