Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, conventional methods tend to use a two-stage sampling paradigm, in which the search space needs to be uniformly explored with an inefficient preliminary sampling phase. In this paper, we propose a novel sampling-based method in the Bayesian filt...

The moving k nearest neighbor query, which computes one’s k nearest neighbor set and maintains it while at move, is gaining importance due to the prevalent use of smart mobile devices such as smart phones. Safe region is a popular technique in processing the moving k nearest neighbor query. It is a region where the movement of the query object does not cause the current k nearest neighbor set t...

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions due to the curse-ofdimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose an adaptive nearest neighbor classification method to try to minimize bias. We use quasiconformal transformed kernels t...

For object recognition based on nearest neighbor search of local descriptors such as SIFT, it is important to keep the nearest neighbor search efficient to deal with a huge number of descriptors. In this report we propose a new method of efficient recognition based on the observation that the level of accuracy of nearest neighbor search for correct recognition depends on images to be recognized...

This paper proposed a new weighted KNN data filling algorithm based on grey correlation analysis (GBWKNN) by researching the nearest neighbor of missing data filling method. It is aimed at that missing data is not sensitive to noise data and combined with grey system theory and the advantage of the K nearest neighbor algorithm. The experimental results on six UCI data sets showed that its filli...

In real-world applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. One method to tackle this problem consists to resample the original training set, either by over-sampling the minority class and/or...

This paper presents a multimedia join operator that is carried out through the method of the nearest neighbor search. In contrast to related approaches that utilizes a similarity function to perform a join between two instances of the input tables, we adopt the more flexible and widely used nearest neighbor method. First, we introduce a simple nearest neighbor search algorithm based on an neste...

Over the last decade, an immense amount of data has become available. From collections of photos, to genetic data, and to network traffic statistics, modern technologies and cheap storage have made it possible to accumulate huge datasets. But how can we effectively use all this data? The ever growing sizes of the datasets make it imperative to design new algorithms capable of sifting through th...

For object recognition based on nearest neighbor search of local descriptors such as SIFT, it is important to make the nearest neighbor search efficient to deal with a huge number of descriptors. In this report we propose a new method of efficient recognition based on the observation that the level of accuracy of nearest neighbor search for correct recognition depends on images to be recognized...