We present a new approach for approximate nearest neighbor queries for sets of high dimensional points under any L t-metric, t = 1; : : : ; 1. The proposed algorithm is eecient and simple to implement. The algorithm uses multiple shifted copies of the data points and stores them in up to (d + 1) B-trees where d is the dimensionality of the data, sorted according to their position along a space ...

The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-dis...

The submitted paper deals with the application of lossy compression to two-dimensional curves scanned grey-scale images. A new algorithm based on correlation optimization used for Space Filling Curve (SFC) determination is presented. The resulting 1-dimensional image representation provides a higher neighbour pixel similarity, similar to other methods [6]. One of such methods is the Peano-Hilbe...

A practical method for creating a high dimensional index structure that adapts to the data distribution and scales well with the database size, is presented. Typical media descriptors, such as texture features, are high dimensional and are not uniformly distributed in the feature space. The performance of many existing methods degrade if the data is not uniformly distributed. The proposed metho...

In this paper, we propose a method for shape-similarity search of 3D polygonal-mesh models. The system accepts triangular meshes, but tolerates degenerated polygons, disconnected component, and other anomalies. As the feature vector, the method uses a combination of three vectors, (1) the moment of inertia, (2) the average distance of surface from the axis, and (3) the variance of distance of t...

In Content-Based Image Retrieval systems it is important to use an efficient indexing technique in order to perform and accelerate the search in huge databases. The used indexing technique should also support the high dimensions of image features. In this paper we present the hierarchical index NOHIS-tree (Non Overlapping Hierarchical Index Structure) when we scale up to very large databases. W...

The introduction of 2D array ultrasound transducers enables the instantaneous acquisition of ultrasound volumes in the clinical practice. The next step coming along is the combination of several scans to create compounded volumes that provide an extended field-of-view, so called mosaics. The correct alignment of multiple images, which is a complex task, forms the basis of mosaicing. Especially ...

Retrieving similar complex documents such as images, sounds, DNA sequences, from within a large collection is an issues of main importance. While content modeling and retrieval algorithms tends to perform more and more efficiently, the methods to access the documents through their abstraction in form of high-dimensional feature vectors perform still poorly. In this report we detail the differen...

In this paper we present a clustering and indexing paradigm (called Clindex) for highdimensional search spaces. The scheme is designed for approximate searches, where one wishes to nd many of the data points near a target point, but where one can tolerate missing a few near points. For such searches, our scheme can nd near points with high recall in very few IOs and performs signi cantly better...

In this paper, a new method of representing two-dimensional shapes using fuzzy-symbolic features and a similarity measure defined over fuzzy-symbolic features useful for clustering shapes is proposed. A k-mutual nearest neighborhood approach for clustering two-dimensional shapes is presented. The proposed shape representation scheme is invariant to similarity transformations and the clustering ...