scholarly journals An Efficient Medical Content Based Image Retrieval with Lenient Relevance Feedback

2018 ◽  
Vol 7 (3.6) ◽  
pp. 175 ◽  
Author(s):  
R I. Heaven Rose ◽  
A C. Subajini
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Early Stage ◽  
Human Perception ◽  
Feedback System ◽  
Content Based Image Retrieval ◽  
Research Issues ◽  
Retrieval Time ◽  
System User ◽  
High Level

Content Based Image Retrieval (CBIR) for medical imageries is still in its early stage.  There are many challenging research issues.  Retrieve similar images only is the current problem in medical CBIR. One idea to solve this difficult is minimizing the gap among two descriptions i.e. low level extracted features of image and high level human perception of image.  There are various Relevance Feedback (WF) methods have been considered to minimize the semantic gap in medical CBIR system. But most of them were deals with hard Feedback. In Hard Feedback system user can interact with the system in one query session. We recommend to aid the usage of lenient relevance response to better capture the intention of users. The meta-knowledge mined from multiple user’s experience be able to   increase the precision of subsequent image recovery results. Here we suggest an algorithm to mine lenient association rules from the group of suggestion i.e. image weight value given by the user. To reduce the amount of strong rules we offer two rule lessening techniques related to redundancy detection and confidence quantization.  Best first search and Binary search methods are similarly applied to advance the procedure of weight interface. The effectiveness of the offered system is assessed regarding precision and average retrieval time. The experimental results on medical images display that the proposed method is able to improve the accuracy of medical CBIR system and reduces the retrieval time than other usual methods.  

scholarly journals A Journey from basic Image Features to Lofty Human Intelligence in Content-based Image Retrieval: Motivation, Applications and Future Trends

2020 ◽  
Vol 9 (2) ◽  
pp. 990-998
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Similarity Measures ◽  
Human Perception ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Future Trends ◽  
Research Issues ◽  
Basic Image ◽  
High Level

Due to a remarkable increase in the complexity of the multimedia content, there is a cumulative enhancement of digital images both online and offline. For the purpose of retrieving images from a vast storehouse of images, there is an urgent requirement of an effectual image retrieval system and the most effective system in this domain is denoted as content-based image retrieval (CBIR) system. CBIR system is generally based on the extraction of basic image attributes like texture, color, shape, spatial information, etc. from an image. But, there exists a semantic gap between the basic image features and high-level human perception and to reduce this gap various techniques can be used. This paper presents a detailed study about the various basic techniques with an emphasis on different intelligent techniques like, the usage of machine learning, deep learning, relevance feedback, etc., which can be used to achieve a high level semantic information in CBIR systems. In addition, a detailed outline regarding the framework of a basic CBIR system, various benchmark datasets, similarity measures, evaluation metrics have been also discussed. Finally, solution to some research issues and future trends have also been given in this paper.

scholarly journals Automatic content-based image retrieval using hierarchical clustering algorithms

2021 ◽  
Author(s):  
Kambiz Jarrah
Keyword(s):  
Image Retrieval ◽  
Hierarchical Clustering ◽  
Relevance Feedback ◽  
Clustering Algorithms ◽  
User Interaction ◽  
Content Based Image Retrieval ◽  
Feature Weight ◽  
High Level ◽  
Tree Map ◽  
Self Organizing

The overall objective of this thesis is to present a methodology for guiding adaptations of an RBF-based relevance feedback network, embedded in automatic content-based image retrieval (CBIR) systems, through the principle of unsupervised hierarchical clustering. The main focus of this thesis is two-fold: introducing a new member of Self-Organizing Tree Map (SOTM) family, the Directed self-organizing tree map (DSOTM) that not only provides a partial supervision on cluster generation by forcing divisions away from the query class, but also presents an objective verdict on resemblance of the input pattern as its tree structure grows; and using a base-10 Genetic Algorithm (GA) approach to accurately determine the contribution of individual feature vectors for a successful retrieval in a so-called "feature weight detection process." The DSOTM is quite attractive in CBIR since it aims to reduce both user workload and subjectivity. Repetitive user interaction steps are replaced by a DSOTM module, which adaptively guides relevance feedback, to bridge the gap between low-level image descriptors and high-level semantics. To further reduce this gap and achieve an enhanced performance for the automatic CBIR system under study, a GA-based approach was proposed in conjunction with the DSOTM. The resulting framework is referred to as GA-based CBIR (GA-CBIR) and aims to import human subjectivity by automatically adjusting the search process to what the system evolves "to believe" is significant content within the query. In this engine, traditional GA operators work closely with the DSOTM to better attune the apparent discriminative characteristics observed in an image by a human user.

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.

Research on Related Technology of Content-Based Image Retrieval

2013 ◽  
Vol 448-453 ◽  
pp. 3616-3620
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Bai Chuan Li
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
The Novel ◽  
Effective Solution ◽  
Retrieval Accuracy ◽  
Novel Method ◽  
Feedback Method ◽  
High Level

Content-Based Image Retrieval (CBIR) system existed a gap between high-level concepts and low-level features. As an effective solution, the Relevance Feedback (RF) technique has been used on many CBIR systems to improve the retrieval precision. In order to further improve convergence speed and retrieval accuracy, a novel relevance feedback method was proposed. According to feedback from user, image feature was weighted and adjusted in the novel method.

scholarly journals Comparing the impact of Clustering with Content Based Image Retrieval Approaches for Plant Identification

2013 ◽  
Vol 9 (1) ◽  
pp. 985-994
Author(s):  
Komal Asrani ◽  
Renu Jain
Keyword(s):  
Image Retrieval ◽  
Processing Time ◽  
Search Time ◽  
Human Perception ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Plant Identification ◽  
Retrieval Time ◽  
The Impact ◽  
Leaf Image

Contour Based retrieval of images is an active and challenging field of research.  Among various parameters available for contour based image retrieval, shape is considered an important aspect because it is closest to the human perception. Most of the shape based image retrieval methods require large processing time for generating accurate results due to huge database. To reduce the search time, we have divided the database into clusters on the basis of eccentricity of leaf using K-Means approach. After making the clusters, different contour based approaches are applied for leaf/plant identification and results are compared.  The leaf image is processed to generate feature vectors which are stored in database.  We have used Swedish leaf image database (SLID) consisting of 15 species with 75 leaves per class and total of 1125 leaf images. In this paper, we compare results of contour based retrieval approaches with and without clustering. From these results, it is found that by incorporating clustering, performance of contour based retrieval approaches remains same but retrieval time is reduced.

scholarly journals Association-Based Image Retrieval

2011 ◽  
pp. 379-406
Author(s):  
Arun Kulkarni ◽  
Leonard Brown
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Human Perception ◽  
Human Memory ◽  
Image Features ◽  
Multimedia Data ◽  
Query Image ◽  
Storage And Retrieval ◽  
Image Storage And Retrieval ◽  
High Level

With advances in computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content-based image retrieval (CBIR) systems have been developed in the last decade. A Typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the database with similar features. Recent advances in CBIR systems include relevance feedback based interactive systems. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. CBIR systems with relevance feedback are more efficient than conventional CBIR systems; however, these systems depend on human interaction. In this chapter, we describe a new approach for image storage and retrieval called association-based image retrieval (ABIR). The authors try to mimic human memory. The human brain stores and retrieves images by association. They use a generalized bi-directional associative memory (GBAM) to store associations between feature vectors that represent images stored in the database. Section I introduces the reader to the CBIR system. In Section II, they present architecture for the ABIR system, Section III deals with preprocessing and feature extraction techniques, and Section IV presents various models of GBAM. In Section V, they present case studies.

Image Retrieval with Relevance Feedback using SVM Active Learning

2016 ◽  
Vol 6 (6) ◽  
pp. 3238 ◽  
Author(s):  
Giang Truong Ngo ◽  
Tao Quoc Ngo ◽  
Dung Duc Nguyen
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Effective Feedback ◽  
General Relevance ◽  
High Level ◽  
Widespread Interest

<pre>In content-based image retrieval, relevant feedback is studied extensively <br />to narrow the gap between low-level image feature and high-level semantic <br />concept. In general, relevance feedback aims to improve the retrieval <br />performance by learning with user's <span>judgements</span> on the retrieval results. <br />Despite widespread interest, but feedback related technologies are often <br />faced with a few limitations. One of the most obvious limitations is often <br />requiring the user to repeat a number of steps before obtaining the <br />improved search results. This makes the process inefficient and tedious <br />search for the online applications. In this paper, a effective feedback <br />related scheme for content-based image retrieval is proposed. First, a <br />decision boundary is learned via Support Vector Machine to filter the <br />images in the database. Then, a ranking function for selecting the most <br />informative samples will be calculated by defining a novel criterion that <br />considers both the scores of Support Vector Machine function and similarity<br />metric between the "ideal query" and the images in the database. The <br />experimental results on standard <span>datasets</span> have showed the effectiveness <br />of the proposed method.</pre>

scholarly journals Image Retrieval with Relevance Feedback using SVM Active Learning

2016 ◽  
Vol 6 (6) ◽  
pp. 3238
Author(s):  
Giang Truong Ngo ◽  
Tao Quoc Ngo ◽  
Dung Duc Nguyen
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Effective Feedback ◽  
General Relevance ◽  
High Level ◽  
Widespread Interest

<pre>In content-based image retrieval, relevant feedback is studied extensively <br />to narrow the gap between low-level image feature and high-level semantic <br />concept. In general, relevance feedback aims to improve the retrieval <br />performance by learning with user's <span>judgements</span> on the retrieval results. <br />Despite widespread interest, but feedback related technologies are often <br />faced with a few limitations. One of the most obvious limitations is often <br />requiring the user to repeat a number of steps before obtaining the <br />improved search results. This makes the process inefficient and tedious <br />search for the online applications. In this paper, a effective feedback <br />related scheme for content-based image retrieval is proposed. First, a <br />decision boundary is learned via Support Vector Machine to filter the <br />images in the database. Then, a ranking function for selecting the most <br />informative samples will be calculated by defining a novel criterion that <br />considers both the scores of Support Vector Machine function and similarity<br />metric between the "ideal query" and the images in the database. The <br />experimental results on standard <span>datasets</span> have showed the effectiveness <br />of the proposed method.</pre>

scholarly journals Automatic content-based image retrieval using hierarchical clustering algorithms

2021 ◽  
Author(s):  
Kambiz Jarrah
Keyword(s):  
Image Retrieval ◽  
Hierarchical Clustering ◽  
Relevance Feedback ◽  
Clustering Algorithms ◽  
User Interaction ◽  
Content Based Image Retrieval ◽  
Feature Weight ◽  
High Level ◽  
Tree Map ◽  
Self Organizing

The overall objective of this thesis is to present a methodology for guiding adaptations of an RBF-based relevance feedback network, embedded in automatic content-based image retrieval (CBIR) systems, through the principle of unsupervised hierarchical clustering. The main focus of this thesis is two-fold: introducing a new member of Self-Organizing Tree Map (SOTM) family, the Directed self-organizing tree map (DSOTM) that not only provides a partial supervision on cluster generation by forcing divisions away from the query class, but also presents an objective verdict on resemblance of the input pattern as its tree structure grows; and using a base-10 Genetic Algorithm (GA) approach to accurately determine the contribution of individual feature vectors for a successful retrieval in a so-called "feature weight detection process." The DSOTM is quite attractive in CBIR since it aims to reduce both user workload and subjectivity. Repetitive user interaction steps are replaced by a DSOTM module, which adaptively guides relevance feedback, to bridge the gap between low-level image descriptors and high-level semantics. To further reduce this gap and achieve an enhanced performance for the automatic CBIR system under study, a GA-based approach was proposed in conjunction with the DSOTM. The resulting framework is referred to as GA-based CBIR (GA-CBIR) and aims to import human subjectivity by automatically adjusting the search process to what the system evolves "to believe" is significant content within the query. In this engine, traditional GA operators work closely with the DSOTM to better attune the apparent discriminative characteristics observed in an image by a human user.

Sign in / Sign up

Export Citation Format

Share Document