Digital image processing is the fastest growing computer-based technology. It plays a vital role in computer vision and image processing tasks and is generally used for various purposes like medical diagnosis, robotics, remote sensing, industrial inspection etc... Some major operations performed on an image to extract some useful information that corresponds to the key stages in the digital ima...

In this thesis we describe the design of the Image Clustering features associated with the Terrascope system. TerraScope is an earth science data middleware system that was designed to facilitate collaboration among a set of data repositories (peers) who wish to provide their geospatial data through an integrated portal. Individual Terrascope repositories are designed to locally store images re...

This paper presents computationally efficient object detection, matching and categorization via Agglomerative Correspondence Clustering (ACC). We implement ACC for feature correspondence and object-based image matching exploiting both photometric similarity and geometric consistency from local invariant features. Objectbased image matching is formulated here as an unsupervised multi-class clust...

In this paper, we present a new learning scenario, heterogeneous transfer learning, which improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is provided. In the past, we have classified Chinese text documents using English training data under the heterogeneous transfer learning framework. In this pape...

The paper presented a reversible image authentication scheme for VQ images using codebook clustering. The proposed method not only locates the alterations of VQ images but also recover the indexes of VQ image without any distortion from the marked VQ indexes after the hidden authentication bits have been extracted. The method uses the codebook clustering to generate four fixed-size small codebo...

This paper proposes an approach for the segmentation of color images using CIELab color space and Ant based clustering. Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. The objective of segmentation is to change the image into meaningful form that is easier to analyze. This paper elaborat...

Abstract— Fractal based CBIR is based on the self similarity fundamentals of fractals. Mathematical and natural fractals are the shapes whose roughness and fragmentation neither tend to vanish, nor fluctuate, but remain essentially unchanged as one zooms in continually and examination is refined. Since an image can be characterized by its fractal code, a fractal code can therefore be used as a ...

Genetic Algorithm (GA) is a stochastic randomized blind search and optimization technique based on evolutionary computing that has already been proved to be robust and effective from its outcome in solving problems from variety of application domains. Clustering is a vital technique to extract meaningful and hidden information from the datasets. Clustering techniques have a broad field of appli...

Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Image segmentation using K-mean clustering for finding tumor in medical applicati...

ABSTARCT: The processing of whole image gives the inefficient and impractical results. Segmentation is the process which results in set of images that cover the entire image. The task of Clustering is an important aspect which is widely used in image segmentation and other areas. In this paper, we study spectral clustering algorithm which clusters data using eigenvectors of similarity matrix. T...