scholarly journals Image salvage based on visual courtesy model using ROI

2018 ◽  
Vol 7 (2.26) ◽  
pp. 63
Author(s):  
K Deepa ◽  
K Priyanka
Keyword(s):  
Image Retrieval ◽  
Teacher Learning ◽  
Image Data ◽  
Saliency Map ◽  
Regions Of Interest ◽  
Content Based Image Retrieval ◽  
Matching Method ◽  
Visual Objects ◽  
Retrieval Systems ◽  
Definition Of

The process of demonstrating, organizing and evaluating the pictures regarding the information despite of evaluating pictures is the field of Content Based Image Retrieval (CBIR). Here we work on the salvage of images based not on keywords or explanations but on features haul out directly from the image data. The well-organized algorithms of salvage algorithms are already proposed. Content Based Image Retrieval has replaced Text Based Image Retrieval. CBIR is processed by more methods and research scientists are working to improve the accuracy of the technique. The project presents that the ROI from an image is retrieved and it retains the image based on Teacher Learning Based Optimization genetic algorithm. The retrieval of the image improves the efficiency based on two metrics such as precision and recall which is the main advantage of the project. The issue of Content Based Image Retrieval systems to provide the semantic gap and to determine the variation between the structure of visual objects and definition of semantics. From the human visual system the visual courtesy is more projected for the purpose of Content Based Image Retrieval. The new similarity based matching method is described based on the saliency map which retains the courtesy values and the regions of interest are hauled out. 

scholarly journals A State of Art on Content Based Image Retrieval Systems

2019 ◽  
Vol 8 (2S4) ◽  
pp. 297-301
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Data ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Query Image ◽  
Indexing Methods ◽  
Retrieval Systems ◽  
State Of Art

The digital image data is quick expanding in capacity and heterogeneity. The customary information retrieval approaches are cannot fulfill the client's need, so there isneed to present a proficient framework for Content Based Image Retrieval(CBIR). The CBIR is an appealing wellspring of precise and quick retrieval. CBIR goes for discovering imagedatabases for explicit images that are like a given query image dependent on its features.In this paper the methodology of content based image retrieval are examined, investigated and thought about. Here, the different image substance, for example, colour, texture and shape features are mined by utilizing differentfeature extraction procedures, and furthermore extraordinary distance measures, Relevance Feedback (RF) and indexing methods are used to improve the execution of the CBIR system.The existing exploration strategies are talked about with their benefits and negative marks, so the further research works can be focused more.

A Perceptual Approach for Image Representation and Retrieval

2011 ◽  
pp. 121-141
Author(s):  
Noureddine Abbadeni
Keyword(s):  
Image Retrieval ◽  
Image Representation ◽  
Rank Correlation ◽  
Human Perception ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Data Set ◽  
Psychometric Method ◽  
Results Merging ◽  
Spearman Rank Correlation Coefficient

This chapter describes an approach based on human perception to content-based image representation and retrieval. We consider textured images and propose to model the textural content of images by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast and busyness. The proposed computational measures are based on two representations: the original images representation and the autocovariance function (associated with images) representation. The correspondence of the proposed computational measures to human judgments is shown using a psychometric method based on the Spearman rank-correlation coefficient. The set of computational measures is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results show a strong correlation between the proposed computational textural measures and human perceptual judgments. The benchmarking of retrieval performance, done using the recall measure, shows interesting results. Furthermore, results merging/fusion returned by each of the two representations is shown to allow significant improvement in retrieval effectiveness.

Issues and Techniques to Mitigate the Performance Gap in Content-Based Image Retrieval Systems

2011 ◽  
pp. 60-83
Author(s):  
Agma J. M. Traina ◽  
Caetano Traina ◽  
Robson Cordeiro ◽  
Marcela Ribeiro ◽  
Paulo M. Azevedo-Marques
Keyword(s):  
Image Retrieval ◽  
Poor Quality ◽  
Content Based Image Retrieval ◽  
Performance Gap ◽  
Similarity Queries ◽  
Access Methods ◽  
Retrieval Systems ◽  
Computational Support ◽  
Indexing And Retrieval ◽  
Key Aspects

This chapter discusses key aspects concerning the performance of Content-based Image Retrieval (CBIR) systems. The so-called performance gap plays an important role regarding the acceptability of CBIR systems by the users. It provides a timely answer to the actual demand for computational support from CBIR systems that provide similarity queries processing. Focusing on the performance gap, this chapter explains and discusses the main problems currently under investigation: the use of many features to represent images, the lack of appropriate indexing structures to retrieve images and features, deficient query plans employed to execute similarity queries, and the poor quality of results obtained by the CBIR system. We discuss how to overcome these problems, introducing techniques such as how to employ feature selection techniques to beat the “dimensionality curse” and how to use proper access methods to support fast and effective indexing and retrieval of images, stressing the importance of using query optimization approaches.

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