In this paper we present scalable algorithms for image retrieval based on color. Our solution for scalability is to cluster the images in the database into groups of images with similar color content. At search time the query image is first compared with the pre-computed clusters, and only the closest set of clusters is further examined by comparing the query image to the images in that set. Th...

The goal of this survey is to use different Clustering techniques to perform image segmentation. Clustering means grouping of images which share some common attributes. The purpose of clustering is to get meaningful result, effective storage and fast retrieval in various areas. The clustering methods are mainly divided into: hierarchical, partitioning, densitybased, model-based, grid-based, and...

Fuzzy clustering based on generalized entropy is studied. By introducing the generalized entropy into objective function of fuzzy clustering, a unified model is given for fuzzy clustering in this paper. Then fuzzy clustering algorithm based on the generalized entropy is presented. At the same time, by introducing the spatial information of image into the generalized entropy fuzzy clustering alg...

In this paper we present a method for unsupervised clustering of image databases. The method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle. Image archives are clustered such that the mutual information between the clusters and the image content is maximally preserved. The IB principle is applied to both discrete and continuous image...

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace has been proven be powerful for exploiting intrinsic relationship between data points. Despite impressive performance in clustering, traditional subspace methods often ignore inherent structural information among In this article, we revisit with graph convolution and present novel framewo...