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Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
State-of-the-art pattern recognition methods have difficulties dealing with problems where the dimension of the output space is large. In this article, we propose a framework based on deep architectures (e. g. deep neural networks) in order to deal with this issue. Deep architectures have proven to be efficient for high dimensional input problems such as image classification, due to their ability...
This paper presents an algorithm for object localization and segmentation. The algorithm uses machine learning, and statistical and combinatorial optimization tools to build a tracker that is robust to noise and occlusions. The method is based on a novel energy formulation and its dual use for object localization and segmentation. The energy uses kernel principal component analysis to incorporate...
Aiming at the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm based on RS (rough set) attribute reduction and transductive support vector machine (TSVM) has been presented-RSTSVM-MIL algorithm. This algorithm regards the whole image as a bag, and the low-level visual feature of the segmented regions as instances, in order to transform every...
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category learning, the active selection problem is particularly complex: a single image will typically contain multiple object labels, and an annotator could provide multiple types of annotation (e.g., class labels, bounding boxes,...
Currently, feather and down category recognition is often done by man with a microscope, but this method has some disadvantages. So a feather and down category recognition system is proposed in this paper, and feather and down category recognition is done by computer automatically. After the image processing and segmentation using GA, the triangle node of two-value image of feather is to be recognized...
We present a general approach to temporal media segmentation using supervised classification. Given standard low-level features representing each time sample, we build intermediate features via pairwise similarity. The intermediate features comprehensively characterize local temporal structure, and are input to an efficient supervised classifier to identify shot boundaries. We integrate discriminative...
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