scholarly journals Determination of Image Features for Content-based Image Retrieval using Interactive Genetic Algorithm

2014 ◽  
Vol 89 (17) ◽  
pp. 13-17
Author(s):  
Sharvari M.Waikar ◽  
K.B.Khanchandani K.B.Khanchandani
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Interactive Genetic Algorithm

Automatic Feature Weight Assignment Based on Image Retrieval Using Genetic Algorithm

2014 ◽  
Vol 931-932 ◽  
pp. 1402-1406 ◽  
Author(s):  
Purichaya Srisook ◽  
Kata Praditwong
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Equal Weight ◽  
Optimal Weight ◽  
Optimal Value ◽  
Feature Weight

The work proposes the new method to increase an efficiency of a Content-based Image Retrieval (CBIR) system. For combining many image features, the optimal weight of each feature is required. To find the optimal value of the feature, this work uses Genetic Algorithm (GA). An image is represented as color, shape and texture features. The experiment compares the results from the system with equal weight values and the system with the weights provided by GA. Evaluation shows the robustness and efficiency of the proposed technique.

scholarly journals Implementation of early and late fusion methods for content-based image retrieval

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 281-302
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

A Content-Based Approach to Medical Images Retrieval

2013 ◽  
Vol 8 (2) ◽  
pp. 15-27
Author(s):  
Mana Tarjoman ◽  
Emad Fatemizadeh ◽  
Kambiz Badie
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Medical Decision ◽  
Content Based Image Retrieval ◽  
Inference System ◽  
Medical Decision Support ◽  
Neuro Fuzzy

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency.

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