Journal:Informatica
Volume 25, Issue 4 (2014), pp. 563–580
Abstract
Abstract
Clustering is one of the better known unsupervised learning methods with the aim of discovering structures in the data. This paper presents a distance-based Sweep-Hyperplane Clustering Algorithm (SHCA), which uses sweep-hyperplanes to quickly locate each point’s approximate nearest neighbourhood. Furthermore, a new distance-based dynamic model that is based on -tree hierarchical space partitioning, extends SHCA’s capability for finding clusters that are not well-separated, with arbitrary shape and density. Experimental results on different synthetic and real multidimensional datasets that are large and noisy demonstrate the effectiveness of the proposed algorithm.
Journal:Informatica
Volume 23, Issue 4 (2012), pp. 645–663
Abstract
This paper builds on a novel, fast algorithm for generating the convex layers on grid points with linear time complexity. Convex layers are extracted from the binary image. The obtained convex hulls are characterized by the number of their vertices and used as representative image features. A computational geometric approach to near-duplicate image detection stems from these features. Similarity of feature vectors of given images is assessed by correlation coefficient. This way, all images with closely related structure and contents can be retrieved from large databases of images quickly and efficiently. The algorithm can be used in various applications such as video surveillance, image and video duplication search, or image alignment. Our approach is rather robust up to moderate signal-to-noise ratios, tolerates lossy image compression, and copes with translated, rotated and scaled image contents.
Journal:Informatica
Volume 23, Issue 1 (2012), pp. 47–63
Abstract
This paper considers a new method for reconstructing deliberately-corrupted pixels in raster images. Firstly, a faster approach for reconstructing corrupted pixels is proposed by applying a processing-circle instead of a processing-square. It is shown that the obtained quality of the reconstructed image is no worse because of this. The quality of the reconstruction is further improved by controlling the pixel corrupting process within the input image. It is shown that a combination of the processing-circle approach and data-dependent corruption reduces the reconstruction time, and the mistakes of the reconstructed pixels.