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In this paper, we introduce a simple approach for detecting enteromorpha based on statistical learning of image features using support vector machines (SVM). The approach first classifies an enteromorpha image into two classes: enteromorpha and background. Then it extracts features from those two classes and uses them for training the SVM model. Finally, the predicting process is carried out in a...
In this paper, we propose a novel static hand gesture recognition method, which is based on a new support vector machine (abbreviated as SVM) classifier. SVM is a classification method based on statistics theory. Typical SVMs can be sufficient to deal with small scale data, but these methods cause a lot of computation in quadratic programming while dealing with non-linear problems. SVM combined with...
In this study, segmentation of color image is performed by supervised classification method based on hybrid color space. We define a kind of color space by selecting a set of color components which can belong to any of the different classical color spaces. We propose to classify pixels represented in the hybrid color space which is specifically designed to yield the best discrimination between the...
Semantic Image Annotation is a difficult task in Annotation Based Image Retrieval (ABIR) systems. Several techniques proposed in the past were lagging in efficiency and robustness. In this paper we are proposing a novel technique for automatically annotating multi-object images with higher accuracy. The colour entropy is used to eliminate the image background, and then we applied normalized cut principle...
With the fast development of video semantic analysis, there has been increasing attention to the typical issue of the semantic analysis of soccer program. Based on the color feature analysis, this paper focuses on the video shot segmentation problem from the perspective of semantic analysis, i.e. the semantic shot segmentation. Most existing works segment and classify the shot by using the dominant...
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