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Person reidentification across nonoverlapping camera views is a rather challenging task. Due to the difficulties in obtaining identifiable faces, clothing appearance becomes the main cue for identification purposes. In this paper, we present a comprehensive study on clothing attributes assisted person reidentification. First, the body parts and their local features are extracted for alleviating the...
Sentence similarity compute is an important part in question answering system based on frequency asking questions. The accuracy of the existing sentence similarity algorithm needs to be improved, so this paper presents a revised question similarity compute method. We combine the word order feature with vector space model algorithm. When we use the VSM to compute the question similarity, we propose...
The orientation of sentiment words plays an important role in the sentiment analysis, but existing methods have difficulty in classifying the orientation of Chinese words, especially for the newly emerged words in Internet. Most approaches are mining the association between sentiment words and seed words using the big corpora and manually labeled seed words with definite orientation. But less work...
In this paper, we present a model-based document information content extraction approach and perform in-depth evaluation based on clients' relevance. Real-world users i.e., clients first provide a set of key fields from the document image which they think are important. These are used to represent a graph where nodes (i.e., fields) are labelled with dynamic semantics including other features and edges...
The RLS-MARS (Regularized Least Squares-Multi Angle Regression and Shrinkage) feature selection model is used to select the relevant information, in which both, the keeping and the leaving-out of the regularizer are present. The RLS-MARS model is to find a series of directions in multidimensional space, leading the gradient vectors to change along those directions which would make the gradient matrix's...
It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. For this reason, there has been recent interest in methods that are capable of obtaining a high accuracy even if the size of the training set is not big. The main purpose of text mining techniques...
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