Similarity between Euclidean and cosine angle distance for nearest neighbor queries

neighbor querie

Declarative Semantics for Contradictory Modular Logic Programs

A complex reasoning system can be designed as an interaction between reasoning modules. A module consists of a declaration of exported/imported predicates and a set of rules containing both negation by default and classical negation. A prioritized modular logic program (PMP) consists of a set of modules and a partial order < on the predicate definitions (M, p), where M is a module and p is a predicate exported by M. Because of the classical negation, conflicts may arise within and among modules. The partial order < def denotes the relative reliability of the predicate definitions contributing to the conflict. We present the reliable semantics for PMPs . The goal of the reliable semantics is to draw reliable conclusions from possibly contradictory PMPs

Segmentation and Histogram Generation Using the HSV Color Space for Image Retrieval

We have analyzed the properties of the HSV (Hue, Saturation and Value) color space with emphasis on the visual perception of the variation in Hue, Saturation and Intensity values of an image pixel. We extract pixel features by either choosing the Hue or the Intensity as the dominant property based on the Saturation value of a pixel. The feature extraction method has been applied for both image segmentation as well as histogram generation applications – two distinct approaches to content based image retrieval (CBIR). Segmentation using this method shows better identification of objects in an image. The histogram retains a uniform color transition that enables us to do a window-based smoothing during retrieval. The results have been compared with those generated using the RGB color space. 1

On k-nearest neighbor searching in non-ordered discrete data spaces

A k-nearest neighbor (k-NN) query retrieves k objects from a database that are considered to be the closest to a given query point. Numerous techniques have been proposed in the past for supporting efficient k-NN searches in continuous data spaces. No such work has been reported in the literature for k-NN searches in a non-ordered discrete data space (NDDS). Performing k-NN searches in an NDDS raises new challenges. The Hamming distance is usually used to measure the distance between two vectors (objects) in an NDDS. Due to the coarse granularity of the Hamming distance, ak-NNqueryinanNDDSmayleadtoalargeset of candidate solutions, creating a high degree of nondeterminism for the query result. We propose a new distance measure, called Granularity-Enhanced Hamming (GEH) distance, that effectively reduces the number of candidate solutions for a query. We have also considered using multidimensional database indexing for implementing k-NN searches in NDDSs. Our experiments on synthetic and genomic data sets demonstrate that our index-based k-NN algorithm is effective and efficient in finding k-NNs in NDDSs.

Economics 1090-9443 OCLC FirstSearch Electronic Collections Online OCLC Online Computer Library Center Inc. Research in Higher Education 0361-0365 OCLC FirstSearch Electronic Collections Online OCLC Online Computer Library Center Inc

Euclidean distance measure has been used in comparing feature vectors of images, while cosine angle distance measure is used in document retrieval. In this paper, we theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of color image database. We find that the cosine angle distance, in general, works equally well for image databases. We show, for a given query vector, characteristics of feature vectors that will be favored by one measure but not by the other. We compute k-nearest neighbors for query images using both Euclidean and cosine angle distance for a small image database. The experimental data corroborate our theoretical results. 1