The spatio-temporal (ST) position information between local features plays an important role in action recognition task. To use the information, neighborhood-based features are built for describing local ST information around ST interest points. However, traditional methods of constructing neighborhood, such as sub-ST volumetric method and nearest-neighbor-based neighborhood method, ignore the ...

Currently, Nearest-Neighbor approaches (NN) have been applied to large scale real world image data mining. However, the following three disadvantages prevent them from wider application compared to other machine learning methods: (i) the performance is inferior on small datasets; (ii) the performance will degrade for data with high dimensions; (iii) they are heavily dependent on the chosen feat...

The combination of maximal margin classifiers and k-nearest neighbors rule constructing an SVM on the neighborhood of the test sample in the feature space (called kNNSVM), was presented as a promising way of improving classification accuracy. Since no extensive validation of the method was performed yet, in this work we test the kNNSVM method on 13 widely used datasets using four different kern...

We deepen the study of two known neighborhood structures, which here will be called f · k-neighborhood structures and f · q-neighborhood structures, in the context of Šostak fuzzy topological spaces. In particular, we characterize fuzzy topologies by f · k-neighborhood structures. Moreover we introduce and discuss the notions of f · kneighborhood prestructure and f ·m-neighborhood structure in ...

BACKGROUND Individuals and families are relying more on food prepared outside the home as a source for at-home and away-from-home consumption. Restricting the estimation of fast-food access to fast-food restaurants alone may underestimate potential spatial access to fast food. METHODS The study used data from the 2006 Brazos Valley Food Environment Project (BVFEP) and the 2000 U.S. Census Sum...

Topological relativity is a concept of interest in geographic information theory. One way of assessing the importance of topology in spatial reasoning is to analyze commonplace terms from natural language relative to conceptual neighborhood graphs, the alignment structures of choice for topological relations. Sixteen English-language spatial prepositions for regionregion relations were analyzed...

This paper presents methods to improve the probability density estimation in hidden Markov models for phoneme recognition by exploiting the Self-Organizing Map (SOM) algorithm. The advantage of using the SOM is based on the created approximative topology between the mixture densities by training the Gaussian mean vectors used as the kernel centers by the SOM algorithm. The topology makes the ne...

MOTIVATION The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topolo...

ـــــ In [25], we introduce the notion of r-fuzzy neighborhood filters in smooth topological spaces in sense of Gähler [10], and used it to define and study separation axioms , = , , . Here we continue our study of the axioms of separation in smooth topological spaces. Therefore, we introduce the notion of r-fuzzy neighborhood filter at a set. Then by using this notion we define and study separ...

This paper develops a formal model of topological relations between a directed line segment (DLine) and a region in a two-dimensional space. Such model forms a foundation for characterizing movement patterns of an agent with respect to a region. The DLine-region relations are captured by the 9intersection for line-region relations with further distinction of the line’s boundary into two subpart...