Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the pa...

Content-based image retrieval is one of the techniques of image mining. Content-based image retrieval system (CBIR) has been proposed by the medical community to manage the storage and distribution of images to radiologists, physicians, specialists, clinics, and imaging centers. There are three fundamental steps for Content Based Image Retrieval. They are Visual Feature Extraction, Similarity M...

Content-Based Image Retrieval (CBIR), is mainly based on finding images of interest from a large image database using the visual content of the images. Most of the approaches to image retrieval were text-based, where individual images had to be annotated with format. Existing works are based on the performance of a number of clustering algorithms in image retrieval has been analyzed. The propos...

Data clustering techniques are often used to segment the real world images. Unsupervised image segmentation algorithms that are based on the clustering suffer from random initialization. There is a need for efficient and effective image segmentation algorithm, which can be used in the computer vision, object recognition, image recognition, or compression. To address these problems, the authors ...

Background: Since tumors located in thorax region of body mainly move due to respiration, in the modern radiotherapy, there have been many attempts such as; external markers, strain gage and spirometer represent for monitoring patients’ breathing signal. With the advent of fluoroscopy technique, indirect methods were proposed as an alternative approach to extract patients’ breathing signals...

Genetic algorithm utilizing image clustering with merge and split processes which allows minimizing Fisher distance between clusters is proposed. Through experiments with simulation and real remote sensing satellite imagery data, it is found that the proposed clustering method is superior to the conventional k-means and ISODATA clustering methods in comparison to the geographic maps and classif...

Abstract:In image analysis techniques, image segmentation takes a major role for analyzing any type of image. The Kmeans clustering algorithm is one of the widely used algorithm in image segmentation system. This paper proposes the colour data base image segmentation using the L*a*b* colour space and K-means clustering. This work presents a data base image segmentation based on colour features ...

This paper provides a novel Web image clustering methodology based on their associated texts. In our approach, the semantics of Web images are firstly represented into vectors of term-weight pairs. In order to correctly correlate terms to a Web image, the associated text of the Web image is partitioned into semantic blocks according to the semantic structure of the text with respect to the Web ...

Extracting semantic information from images has attracted much attention in the domain of computer vision and image processing. Areas like face recognition, detection, tracking etc. work on identifying semantics in images. In this paper we attempt to cluster images based on their semantic content. The approach involves segmenting the image at different scales and extracting interesting patches ...

This paper addresses the problem of clustering images of objects seen from different viewpoints. That is, given an unlabelled set of images of n objects, we seek an unsupervised algorithm that can group the images into n disjoint subsets such that each subset only contains images of a single object. We formulate this clustering problem under a very broad geometric framework. The theme is the in...