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.

On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search

2010 ◽  
Vol 4 (4) ◽  
pp. 18-36 ◽  
Author(s):  
Jane You ◽  
Qin Li ◽  
Jinghua Wang
Keyword(s):  
Image Retrieval ◽  
Data Warehouse ◽  
Image Data ◽  
Image Feature ◽  
Image Indexing ◽  
Content Based Image Retrieval ◽  
New Approach ◽  
Guided Search ◽  
Dynamic Image ◽  
Face Features

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing. It also provides an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features. Experimental results confirm that the new approach is feasible for content-based image retrieval.

Content-Based Image Retrieval Query Paradigms

2005 ◽  
pp. 556-563 ◽  
Author(s):  
Colin C. Venters ◽  
Richard J. Hartley ◽  
William T. Hewitt
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Image Data ◽  
Data Capture ◽  
Content Based Image Retrieval ◽  
Spatial Relationships ◽  
Classification Schemes ◽  
Technological Advances ◽  
Retrieval Problem ◽  
And Storage

The proliferation in volume of digital image data has exacerbated the general image retrieval problem, creating a need for efficient storage and flexible retrieval of vast amounts of image data (Chang, 1989). Whilst there have been significant technological advances with image data capture and storage, developments in effective image retrieval have not kept pace. Research in image retrieval has been divided into two areas: concept-based image retrieval and content-based image retrieval. The former focuses on the use of classification schemes or indexing terms to retrieve images while the latter focuses on the visual features of the image, such as colour, shape, texture, and spatial relationships.

CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION

2003 ◽  
Vol 03 (01) ◽  
pp. 119-143 ◽  
Author(s):  
ZHIYONG WANG ◽  
ZHERU CHI ◽  
DAGAN FENG ◽  
AH CHUNG TSOI
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Network Model ◽  
Neural Network Model ◽  
Relevance Feedback ◽  
Image Representation ◽  
Tree Structure ◽  
Content Based Image Retrieval ◽  
Adaptive Processing ◽  
Image Content

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.

A NOVEL INDEXING AND ACCESS MECHANISM USING AFFINITY HYBRID TREE FOR CONTENT-BASED IMAGE RETRIEVAL IN MULTIMEDIA DATABASES

2007 ◽  
Vol 01 (02) ◽  
pp. 147-170 ◽  
Author(s):  
KASTURI CHATTERJEE ◽  
SHU-CHING CHEN
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Nearest Neighbor ◽  
Human Perception ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Computational Overhead

An efficient access and indexing framework, called Affinity Hybrid Tree (AH-Tree), is proposed which combines feature and metric spaces in a novel way. The proposed framework helps to organize large image databases and support popular multimedia retrieval mechanisms like Content-Based Image Retrieval (CBIR). It is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. AH-Tree, by being able to introduce the high level semantic image relationship as it is in its index structure, solves the problem of translating the content-similarity measurement into feature level equivalence which is both painstaking and error-prone. Algorithms for similarity (range and k-nearest neighbor) queries are implemented and extensive experiments are performed which produces encouraging results with low I/O and distance computations and high precision of query results.

scholarly journals Feature Extraction and Content Based Image Retrieval for High Resolution Remote Sensing Images

2019 ◽  
Vol 8 (3) ◽  
pp. 8881-8884
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Image Retrieval ◽  
Satellite Images ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Remote Sensing Images ◽  
Image Collection

These are the days where we are very rich in information and poor in data. This is very true in case of image data. Whether it is the case of normal images or satellite images, the image collection is very huge but utilizing those images is of least concern. Extracting features from big images is a very challenging and compute intensive task but if we realize it, it will be very fruitful. CBIR (Content Based Image Retrieval) when used with HRRS (High Resolution Remote Sensing) images will yield with effective data.

scholarly journals Interactive Content Based Image Retrieval using Multiuser Feedback

2017 ◽  
Vol 1 (4) ◽  
pp. 165
Author(s):  
M. Premkumar ◽  
R. Sowmya
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Human Perception ◽  
Content Based Image Retrieval ◽  
Rule Mining ◽  
Query Image ◽  
Feedback Techniques

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

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. 

Fuzzy Techniques for Content-Based Image Retrieval

2018 ◽  
pp. 41-64
Author(s):  
Rose Bindu Joseph P. ◽  
Ezhilmaran Devarasan
Keyword(s):  
Image Retrieval ◽  
Fuzzy Theory ◽  
Spatial Relationship ◽  
Similarity Measures ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Human Reasoning ◽  
Fuzzy Distance ◽  
Image Retrieval System ◽  
Fuzzy Techniques

Content-based image retrieval aims to acquire images from huge databases by analyzing their visual features like color, texture, shape, and spatial relationship. The search for superior accuracy in image retrieval has resulted in concentrating more on semantic gap reduction between the low-level features and high level human reasoning. Fuzzy theory is a prevailing methodology which helps in attaining this goal by using attributes and interpretations similar to human reasoning. The vagueness and impreciseness in image data and the retrieval process can be modeled by fuzzy sets. This chapter analyses fuzzy theoretic approaches in various stages of content-based image retrieval system. Various fuzzy-based feature descriptors are discussed along with different fuzzy classification and indexing algorithms for content-based image retrieval. This chapter also presents an overview of various fuzzy distance and similarity measures for image retrieval. A novel fuzzy theoretic retrieval for finger vein biometric images is also proposed in this chapter with experiment and analysis.

Application of Content-Based Image Retrieval in Medical Image Acquisition

2020 ◽  
pp. 220-240
Author(s):  
Vinayak Majhi ◽  
Sudip Paul
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Image Acquisition ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Extraction Algorithm ◽  
Day By Day ◽  
Acquisition Technique

Content-based image retrieval is a promising technique to access visual data. With the huge development of computer storage, networking, and the transmission technology now it becomes possible to retrieve the image data beside the text. In the traditional way, we find the content of image by the tagged image with some indexed text. With the development of machine learning technique in the domain of artificial intelligence, the feature extraction techniques become easier for CBIR. The medical images are continuously increasing day by day where each image holds some specific and unique information about some specific disease. The objectives of using CBIR in medical diagnosis are to provide correct and effective information to the specialist for the quality and efficient diagnosis of the disease. Medical image content requires different types of CBIR technique for different medical image acquisition techniques such as MRI, CT, PET Scan, USG, MRS, etc. So, in this concern, each CBIR technique has its unique feature extraction algorithm for each acquisition technique.

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