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This paper presents a novel region merging segmentation method for color image based on color and texture distribution features. The segmentation strategy includes two phases. In the first phase, we select initial seed points for super pixels extraction in the texture energy image at average intervals. Then we implement pixels clustering to extract over segmentation regions at local areas using color...
In this paper, we propose a method by engaging the one class support vector machine (OC-SVM) in the identification of diffractive optically variable images (DOVIs). OC-SVM, as a special SVM, can solve the problems of high-dimensional data sets and small sample size (SSS) with positive and negative unbalance training data. Image feature matrix is built by extracting image features from texture aspects...
This paper presents a new human motion recognition method based on motion history image (MHI) and local binary pattern (LBP). MHI describes human motion sequence in one gray level image and LBP extracts its texture features. LBP feature image is built and chi square distance is applied to compute matching cost. Experiments are conducted with encouraging results which show a success of applying LBP...
Applying ant colony optimization algorithm on the remote sensing image classification is a new research topic, and the preliminary experiments showed many promising characters, but there are also some shortcomings such as needing longer computing time and the classification accuracy is not high enough when using single feature of the image. In order to overcome these defects, we propose to combine...
In this paper, we propose to combine the spectral and texture features to compose the multi-feature vectors for the classification of multispectral remote sensing image. It usually is difficult to obtain the higher classification accuracy if only considers one kind feature, especially for the case of different geographical objects have the same spectrum or texture specialty for a multispectral remote...
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