Analysis of Different Clustering Algorithms on Image Databases

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.

2014

Image Mining is a apecial technique used to study different aspects in detail of an image. In case of image mining there are certain algorithms with which different details of an image can be studied. Some of them are k-means, c-means, fuzzy approaches. Out of which we have adopted the technique of c-means clustering to achieve our target. In this algorithm we generally make different clusters on the basis of similarity and differences of images and form an image set. This approach depends on the membership function which clarifies the similarity and dissimilarity between the images. It depends on the mathematical value that specifies the membership function. Values of membership function is either near zero or one. Zero means similarity between images is very less and one means images are highly similar.

As processors become increasingly powerful, and memories become increasingly cheaper, the deployment of large image databases for a variety of applications have now become realizable. Databases of art works, satellite and medical imagery have been attracting more and more users in various professional fields for example, geography, medicine, architecture, advertising, design, fashion, and publishing. Effectively accessing desired images from large and varied image databases is now a necessity. Due to development of multimedia technology and increasing vogue of the computer network, the conventional information retrieval systems are not able to overcome the users' current need. There are various areas in which digital images are used such as-, and government institutions etc. Because of this widespread demand we need to enhance in retrieval precision and minimized retrieval time. The prior methods were only dependent on text based searching instead of its visual feature. Many times just one keyword is redundantly used with more than one images, therefore it leads to erroneous outcomes. Consequently, Content Based Image Retrieval (CBIR) is evolved to defeat the restriction of text based retrieval. Problems which we are identified in the existing image retrieval systems are as follows-How to retrieve the search image accurately, how we can manage a large database of images, how we can make the searching process efficient. In this paper, we will study different content based image retrieval algorithms and provide a way through which we can provide efficient access to image data

IJRCAR, 2014

The field of image retrieval has been an active research area for several decades and has been paid more and more attention in recent years as a result of the dramatic and fast increase in the volume of digital images. Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and an annotated image databases. CBIR systems index the media documents using salient features extracted from the actual media rather than by textual annotations. Query by content is nowadays a very active research field, with many systems being developed by industrial and academic teams. Results performed by these teams are really promising. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. The proposed CBIR technique uses more than one clustering techniques to improve the performance of CBIR. This optimized method makes use of Hierarchical clustering technique to improve the execution time and performance of image retrieval systems in high dimensional sets. In this similarity measure is totally based on colors. In this paper more focus area is the way of combination of clustering technique in order to get faster output of images.

Image retrieval is the basic requirement task in the present scenario. Content Based Image Retrieval is the popular image retrieval system by which the target image to be retrieved based on the useful features of the given image. In other end, image mining is the arising concept which can be used to extract potential information from the general collection of images. Target or close Images can be retrieved in a little fast if it is clustered in a right manner. In this paper, the concepts of CBIR and Image mining have been combined and a new clustering technique has been introduced in order to increase the speed of the image retrieval system.

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

IJCA Proceedings on …, 2012

Image mining presents special characteristics due to the richness of data that an image can show. Effective evaluation of results of image mining by content requires that the user point of view is used on the performance parameters. Comparison among different ...

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.

This paper presents an image retrieval engine based on advanced clustering strategy which improves performance of image retrieval in term of precision, recall, accuracy and retrieval time. This paper also highlights an overview of recently developed image clustering techniques and their real time applications such as Clustering based image binarization in palm leaf manuscripts, Blood Vessel Extraction for retinal images, synthetic aperture radar images, tumor detection in MRI images etc. Clustering is usually used in image processing, data mining, image retrieval, pattern recognition, image segmentation etc. Clustering provides an effective analysis and proper validation of image for retrieval. The proposed system is employed advanced clustering strategy which utilized N-cut clustering, hierarchical and fuzzy k-means clustering techniques. Cluster based image retrieval system search similar images based on clusters that calculated by color, texture and shape and compare similarity with query image clustered feature. Clustered based image retrieval is fast and accurate. Here, K mean clustering form clusters of similar image features and then N-cut arrange these clusters to improve retrieval speed. Hierarchical clustering is applied on retrieved set of images in relevance feedback to improve accuracy. Proposed content based image retrieval system based on novel clustering strategy significantly improves performance of image retrieval. Experimental result reveals that there is outstanding improvement in accuracy, retrieval time, precision and recall of the image retrieval system.

Journal of Electronic Commerce in Organizations, 2018

With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.