One of the main factors that affect the accuracy of intensity-based registration of two-dimensional (2D) X-ray fluoroscopy to three-dimensional (3D) CT data is the similarity measure, which is a criterion function that is used in the registration procedure for measuring the quality of image match. This paper presents a unifying framework for rationally deriving point similarity measures based o...

In real life biomedical classification applications, feature space may be of high dimension in which visualization of class distribution is impossible. Moreover, attributes of features may be numeric, ordinal, categorical or binary. Most of the time, features may be composed of mixed type of attributes. In this paper, the concept of similarity-dissimilarity is extended to various types of attri...

Given two character images, we would like to measure their similarity or difference. Such a similarity or difference measure facilitates the solution to character recognition and handwriting analysis problems. There is, however, no universal definition for similarity measure satisfying wide range of characteristics such as the slant, deformation or other invariant constraints. For this reason, ...

Similarity has been proposed as a fundamental principle underlying mental object representations and capable of supporting cognitive-level tasks such as categorization. However, much of the research has considered connections between similarity and categorization for tasks performed using a single perceptual modality. Considering similarity and categorization within a multimodal context opens u...

Nearest-neighbor search (NN-search) in the feature space is widely used for the similarity retrieval of multimedia information. Each piece of multimedia information is mapped to a vector in a multi-dimensional space where the distance between two vectors (typically, Euclidean distance between the heads of vectors) corresponds to the similarity of multimedia information. Once the feature space i...

The method of self-organizing maps (SOM) is a method of exploratory data analysis used for clustering and projecting multi-dimensional data into a lower-dimensional space to reveal hidden structure of the data. The algorithm used retains local similarity and neighborhood relations between the data items. In some cases we have to compare the structure of data items visualized on two or more self...

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index image’s multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partition’s center in individual space is used as the indexing key, where similarity values in different features are distinguished by differe...

The seminal paper published by Leland et al. triggered a new direction on traffic modeling research, by unveiling the fractal nature of network traffic processes (Leland, Taqqu, Willinger, Wilson, & Bellcore, 1994). These authors experimentally demonstrated that the LAN Ethernet traffic collected in Bellcore Morristown Research and Engineering Center exhibits self-similar properties and burstin...

Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study th...

bifurcations leading to chaos have been investigated in a number of one dimensional dynamical systems by varying the parameters incorporated within the systems. the property hyperbolicity has been studied in detail in each case which has significant characteristic behaviours for regular and chaotic evolutions. in the process, the calculations for invariant set have also been carried out. a bro...