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This article shows the improvement of automatic cartoon classification. Two new visual features - color component and color kind based on region segmentation - are proposed. Compared to traditional HSV color histogram and texture, experiment using the two new features can achieve better result, with less dimensions and higher mining efficiency.
We consider the task of automatic detection and recognition of traffic signs in video. We show that successful off-the-shelf detection (Viola-Jones) and classification (SVM) systems yield unsatisfactory results. Our main concern are high false positive detection rates which occur due to sparseness of the traffic signs in videos. We address the problem by enforcing spatio-temporal consistency of the...
In this paper, we propose a method for fast pedestrian detection in images/videos. Multi-scale orientated (MSO) features are proposed to represent coarse pedestrian contour, on which Adaboost classifiers are trained for pedestrian coarse location. In the fine detection, histogram of oriented gradient (HOG) features and SVM classifiers are employed to precisely classify pedestrians and non-pedestrians...
This paper presents a semantic-based video analysis method and two steps of their classification in the wushu video: Firstly, used of the image frame difference to construct on the basis of background image, and used of threshold settings to determine the body information and background information, This can achieve the elimination of background and exercise the purpose of extracting the human body...
Background modeling is an essential and important part of many high-level video processing applications. Recently, the Support Vector Data Description (SVDD) has been introduced for novelty detection when only one class of data is available, i.e. background pixels. This paper proposes a method to efficiently train an SVDD and compares the performance of this training algorithm with the traditional...
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