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In this paper, we present a robust and fast object tracking algorithm based on sub-region classifiers and compressive tracking. Compared with the original CT algorithm, the tracker can improve the robustness to occlusion, especially long-term occlusion. Firstly, the target region is divided into four sub-regions in a fixed mode. Then a simple but feasible classification and update strategy is used...
To improve the performance of multi-pose face detection, the AdaboostSVM algorithm based on multi-feature fusion is proposed in this paper. Firstly, the Haar-like features and the triangular integral features are introduced and the edge-orientation field features based on morphological gradient are presented. Then, the AdaboostSVM Algorithm based on the above three kinds of features is proposed. The...
Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples can lead inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper,...
In order to solve the reasoning and decision problems when the given information is not sufficient, a mining method for attribute reduction of classification rules based on Rough Fuzzy Set theory is proposed, and its flow chart is also presented. The method can mine hidden association rules in samples and make decisions by using the reduction algorithm of characteristic attribute based on rough set...
The image semantic classification is new focus in the image classification field, the traditional classification algorithm is based on the low level visual features, but there is an enormous semantic gap problem between the low-level visual features and high-level semantic information of images. An image semantic classification approach is proposed based on Kernel PCA Support Vector Machines (KPCA...
This paper presents an implementation of incremental tumor diagnosis algorithm (ITDA) on microarray data for improving diagnostic accuracy of tumor. A classifier (BP or KNN) was used in the algorithm to estimate confidences of a new unlabeled sample in different classes. When one confidence is higher than the threshold, the sample will be labeled; otherwise, the sample will be diagnosed by medical...
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