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Image fingerprinting is regarded as an alternative approach to watermarking in terms of near-duplicate detection application. It consists of feature extraction and feature indexing. Generally, the former is mainly related to discrimination, robustness , and security while the latter closely focuses on the efficiency of fingerprints search. To enable fast fingerprints searching over a very large database,...
Discriminative feature extraction (DFE) is an effective linear dimensionality reduction method for pattern recognition. It improves the recognition performance via optimizing subspace projection axes and classifier parameters simultaneously. In this paper, we propose a nonlinear extension of DFE, called discriminative quadratic feature extraction (DQFE), for which feature vectors are firstly mapped...
The discriminative training of classifiers for handwritten Chinese character recognition (HCCR) is highly demanding in computation due to the large number of categories. The inability of discriminative training with large sample set on personal computers has hindered the accuracy promotion for HCCR. To overcome this problem, we have implemented the training algorithm of discriminative learning quadratic...
Perturbation-based recognition is effective to recover the deformation of handwritten characters and improve the recognition performance by generating multiple distortions and selecting a distortion that best restores character deformation. Considering that the characters in a field undergo similar deformation under a consistent style, we proposed style consistent perturbation for handwritten character...
Connect6 is simple in rules but quite complex while playing. Owing to the great game-tree complexity, the game situation can be changed easily after even one stone put on the board, the players have to change strategies according to different board situations. To make the program more clever, different strategies must be taken into concern. This paper covers about Connect6 and its rules, the design...
To reduce the human effort in labeling the training set for document classification, some learning algorithms ask users to give the representative keywords for each class rather than any labeled documents. The key challenge in such \emph {keyword-labeled classification} is how to learn the high quality classifier with very small number of keywords. In this paper, we propose a novel co-clustering based...
Kernel discriminant analysis (KDA) is a widely used tool for feature extraction. But for high-dimensional multi-class tasks such as face recognition, traditional KDA algorithms have the limitation that the Fisher criterion is nonoptimal with respect to classification rate. Moreover, they suffer from the ldquosmall sample sizerdquo problem. This paper presents a variant of KDA that deals with both...
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