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Due to rapid growth of computerized medical imagery, the research area of medical image classification has been very active for the past decade. This paper presents an approach to achieve high recognition rate from classification of medical x-ray images. The methodology is based on local binary pattern as a feature extraction technique and support vector machine (SVM) as a classifier. This classification...
We compare the scene classification performance of 13 features, including structure, texture and color features. First, image classification are performed using a single feature and the performance of different features are compared. Both the k-nearest-neighbor (KNN) classifier and the support vector machine classifier (SVM) are employed. And for the KNN classifier, we use four different distance...
As datasets grow increasingly large in content-based image and video retrieval, computational efficiency of concept classification is important. This paper reviews techniques to accelerate concept classification, where we show the trade-off between computational efficiency and accuracy. As a basis, we use the Bag-of-Words algorithm that in the 2008 benchmarks of TRECVID and PASCAL lead to the best...
Most recent methods for image classification focus on how to formulate different types of features effectively in a uniform formula. Although these features take on different importance for image classification, most previous work gives the same weight to the features when they are combined. In this paper, we propose an approach to integrate multi-features by following the multiple kernel learning...
In this study, we present a representation based on a new 3D search technique for volumetric human poses which is then used to recognize actions in three dimensional video sequences. We generate a set of cylinder like 3D kernels in various sizes and orientations. These kernels are searched over 3D volumes to find high response regions. The distribution of these responses are then used to represent...
Multiple kernel learning (MKL) approach for selecting and combining different representations of a data is presented. Selection of features from a representation of data using the MKL approach is also addressed. A base kernel function is used for each representation as well as for each feature from a representation. A new kernel is obtained as a linear combination of base kernels, weighted according...
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