∗ This work is done while the author visiting Microsoft Research China. ABSTRACT The shot based classification and retrieval is very important for video database organization and access. In this paper we present a new approach ‘Nearest Feature Line – NFL’ used in shot retrieval. We look key-frames in shot as feature points to represent the shot in feature space. Lines connecting the feature poi...

The K Nearest Neighbors classification method assigns to an unclassified observation the class which obtains the best results after a voting criteria is applied among the observation’s K nearest, previously classified points. In a validation process the optimal K is selected for each database and all the cases are classified with this K value. However the optimal K for the database does not hav...

The relationship between directional derivatives of generalized distance functions and the existence of generalized nearest points in Banach spaces is investigated. Let G be any nonempty closed subset in a compact locally uniformly convex Banach space. It is proved that if the one-sided directional derivative of the generalized distance function associated to G at x equals to 1 or −1, then the ...

This paper describes an enhanced clustering method for 3D point cloud data which is acquired from a laser range scanning system. The proposed method overcomes the instinctive problems of the vertical or horizontal scanning system. The acquired 3D laser range data for an autonomous vehicle has a disadvantage in that the point cloud data from the system is not equally distributed based on distanc...

As a research branch of data mining, clustering, as an unsupervised learning scheme, focuses on assigning objects in the dataset into several groups, called clusters, without any prior knowledge. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most widely used clustering algorithms for spatial datasets, which can detect any shapes of clusters and can automatic...

In a spatial point set, clustering patterns (features) are difficult to locate due to the presence of noise. Previous methods, either using grid-based method or distance-based method to separate feature from noise, suffer from the parameter choice problem, which may produce different point patterns in terms of shape and area. This paper presents the Collective Nearest Neighbor method (CLNN) to ...

We prove conditional near-quadratic running time lower bounds for approximate Bichromatic Closest Pair with Euclidean, Manhattan, Hamming, or edit distance. Specifically, unless the Strong Exponential Time Hypothesis (SETH) is false, for every δ > 0 there exists a constant ε > 0 such that computing a (1 + ε)-approximation to the Bichromatic Closest Pair requires Ω (

We present algorithms for five interdistance enumeration problems that take as input a set S of n points in IRd (for a fixed but arbitrary dimension d) and as output enumerate pairs of points in S satisfying various conditions. We present: an O(n log n + k) time and O(n) space algorithm that takes as additional input a distance δ and outputs all k pairs of points in S separated by a distance of...

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore pre-compute the result of any nearest-neighbor s...

ÐSimilarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of highdimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor se...