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Multidocument summarization has gained popularity in many real world applications because vital information can be extracted within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences and the ranking results rely heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features for...
The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper investigates two common methods of feature extraction on EEG signals, autoregressive (AR) model and approximate...
In this paper, we present a non-symmetry and anti-packing object pattern representation model (NAM). The NAM model codes the geometry of generic object categories as a hierarchy of sub-patterns and each sub-pattern is represented by a rich set of image cues. The sub-pattern-based NAM model is designed to decouple variations due to affine warps and other forms of shape deformations. The combination...
SIFT feature matching algorithm, which has good robustness to image rotation and zoom scale, is a certain degree of stability on the angle changes and affine transformation. It also has the more stable match capacity on images which are taken from any point of view. It has been widely used in the field of wide baseline stereo matching. But the biggest problem is that extracts too many feature points,...
Measuring similarity has a wide range of application in information retrieval, machine translation or other related fields. In this paper, we proposed a text similarity computation based on improved optimal assignment model, which combine the improved Hungarian algorithm with the semantic similarity of terms to obtain the maximum semantic similarity between two documents or between a query and a document...
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