Image clustering for a fuzzy hamming distance based cbir system (original) (raw)

Content based Image Retrieval using Clustering

International Journal of Computer Applications, 2012

This paper presents novel techniques for image retrieval using the clustering features extracted from images based on Row Mean Clustering, Column Mean clustering, Row Mean DCT Clustering ,Column Mean DCT Clustering, Row Mean Wavelet Clustering and Column Mean Wavelet Clustering. The proposed techniques are compared with well known traditional technique such as Hierarchical Clustering. Hierarchical clustering starts by calculating the Euclidean distance measure for all patterns in data set, which is not required to calculate in proposed techniques. Hence number of clusters used for comparison of proposed techniques is less as compared to existing technique (Hierarchical Clustering). All the CBIR techniques are implemented on a database having 665 images spread across 31 classes. The results of proposed techniques have shown performance improvement (higher precision and Recall) as compared to existing technique at reduced computations.

Clustering for Content based Image Retrieval-A Survey

2014

Clustering is the technique of classifying substance into sets of related or unrelated group of objects, basically Clustering is data analysis method for pattern recognition, feature extraction. Clustering perform very important task in CBIR to improve the accuracy in an image retrieval process. General Terms Pattern Recognition

A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets

International Journal, 2011

As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analysed and compared. It also introduced the feature like neuro fuzzy technique, color histogram, texture and edge density for accurate and effective Content Based Image Retrieval System.

Hierarchical clustering techniques and classification applied in Content Based Image Retrieval (CBIR

– This paper presents a study on the effectiveness of hierarchical clustering techniques application and classification for imaging context in the Content-Based Image Retrieval (CBIR). The study has the purpose to compare the obtained results from using different hierarchical clustering algorithms with various input parameters and configurations using two types of comparison techniques. The aims is also to highlight the performance improvements and the costs brought up by the integration of such techniques in the content-based image retrieval.

Implementation of Content Base Image Retrieval Using Clustering Technique

Based Image Retrieval, CBIR system. It outlines the problem, the proposed solution, the final solution and the accomplishments achieved. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this report outlines a description of the primitive features of an image; texture, colour, and shape. These features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features, are then explained. Our final result was a built software application, with an image database, that utilized texture and colour features of the images in the database as the basis of comparison and retrieval. The structure of the final software application is illustrated. Furthermore, the results of its performance are illustrated by a detailed example.

Content-Based Image Retrieval through Clustering

The field of image retrieval has been an active research area for several decades and has gained steady momentum in recent years as a result, large collection of digital images are growing day by day in government, hospitals, banking, etc. Interest in image retrieval has increased in large part due the rapid growth of the World Wide Web. With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large collection of image databases has drastically increased. Query based text retrieval from database is done through tools like SQL, MYSQL etc. In this process we consider constraint based query to retrieve text records from database. But image retrieval is not as easy as query processing. For this proposed system a broad literature survey of data mining technique have been made. Clustering technique has been identified as best suited for this system. In clustering technique there exist algorithms like BIRCH, CURE, DBSCAN, STING, K-Means, K-Medoids etc, out of which K-Means have been chosen as best suited algorithm for the proposed system. In Image retrieval Shape and Color of an object plays an important role. To address such a demand, Content-Based Image Retrieval (CBIR) through Clustering is proposed in this system for implementing CBIR system and identifying cluster of images for medical applications. With this process, the knowledge discovery through pattern recognition is carried out, through which the medical researchers can take predictive measures.

Study on Query Based Clustering Technique for Content Based Image Retrieval

Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 1 Abstract— Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and annotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. An image retrieval system that takes the input query image and retrieves the similar images according to the spatial coordinates and which uses the k means clustering algorithm for its segmentation. Most existing Content Based Image Retrieval based on the images of color, text documents, informative charts, and shape.

Partitioning Based Clustering Method for Image Retrieval

Clustering is a major technique used for grouping of numerical and image data in data mining and image processing applications. Clustering makes the job of image retrieval easy by finding the images as similar as given in the query image. The images are grouped together in some given number of clusters. Image data are grouped on the basis of some features such as color, texture, shape etc. contained in the images in the form of pixels. Content Based Image Retrieval (CBIR) is a collection of techniques for retrieving images from large database. The images are retrieved based on content. The term " content " demonstrate to color, texture and shapes. In this system, color and texture features are retrieved from images. The color features are obtained using Dynamic Color Distribution Entropy of Neighborhoods (D_CDEN). The texture features are obtained using Gray Level Co-occurrence Matrix (GLCM). The clustering technique is presented to solve the above problem. In this system, K-Means and Contribution based clustering techniques are used. The K-Means clustering algorithm optimizes only intra cluster similarity. Contribution based clustering enhance both intra and inter cluster similarity. The experimental results shows the comparison between average dispersion measures for both K-Means and Contribution based clustering.