Study and Analysis of User Desired Image Retrieval

2020 ◽  
Vol 13 ◽  
Author(s):  
John Bosco P ◽  
S Janakiraman
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Retrieval ◽  
State Of The Art ◽  
The State ◽  
Content Based Image Retrieval ◽  
Processing Application ◽  
Detection Techniques ◽  
Digital World ◽  
Color Texture

Background: In the present digital world, Content Based Image Retrieval (CBIR) has gained significant importance. In this context, the image processing technology has become the most sought one, as a result its demand has increased to a large extend. The complex growth concerning computer technology offers a platform to apply the image processing application. Well-known image retrieval techniques suitable for application zone are 1.Text Based Image Retrieval (TBIR) 2. Content Based Image Retrieval (CBIR) and 3.Semantic Based Image Retrieval (SBIR) etc. In recent past, many researchers have conducted extensive research in the field of content-based image retrieval (CBIR). However, many related research studies on image retrieval and characterization have exemplified to be an immense issue and it should be progressively developed in its techniques. Hence, by putting altogether the research conducted in the recent years, this survey study makes a comprehensive attempt to review the state-of –the art in the field. Aims: This paper aims to retrieve similar images according to visual properties, which defined as Shape, color, Texture and edge detection. Objective: To investigate the CBIR to achieve the task because of the essential and fundamentals problems. The present and future trends are addressed to show come contributions and directions and it can inspire more research in the CBIR methods. Result: we present a deep analysis of the state of the art on CBIR methods; we explain the methods based on Color, Texture, and shape, and edge detection with performance evaluation metrics. In addition, we have discussed some significant future research directions reviewed. Methods: This paper has quickly anticipated the noteworthiness of CBIR and its related improvement, which incorporates Edge Detection Techniques, Various sorts of Distance Metric (DM), Performance measurements and various kinds of Datasets. This paper shows the conceivable outcomes to overcome the difficulties concerning re-positioning strategies with an exceptional spotlight on the improvement of accuracy and execution. Discussion: At last, we have proposed another technique for consolidating different highlights in a CBIR framework that can give preferred outcomes over the current strategies.

scholarly journals A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Muhammad Yousuf ◽  
Zahid Mehmood ◽  
Hafiz Adnan Habib ◽  
Toqeer Mahmood ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Image Retrieval ◽  
State Of The Art ◽  
The State ◽  
Content Based Image Retrieval ◽  
Scale Invariant ◽  
Low Contrast ◽  
Large Rotation ◽  
Visual Words ◽  
Features Fusion ◽  
Novel Technique

Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

scholarly journals Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

GLORIA Image Processing: The State of the Art

1990 ◽  
pp. 21-39 ◽  
Author(s):  
R. C. Searle ◽  
T. P. Le Bas ◽  
N. C. Mitchell ◽  
M. L. Somers ◽  
L. M. Parson ◽  
...  
Keyword(s):  
Image Processing ◽  
State Of The Art ◽  
The State

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

Web phishing detection techniques: a survey on the state-of-the-art, taxonomy and future directions

IET Networks ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 235-246 ◽  
Author(s):  
M. Vijayalakshmi ◽  
S. Mercy Shalinie ◽  
Ming Hour Yang ◽  
Raja Meenakshi U.
Keyword(s):  
State Of The Art ◽  
The State ◽  
Future Directions ◽  
Detection Techniques ◽  
Phishing Detection

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

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