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Considering the lower accuracy of existing traffic sign recognition methods, a new traffic sign recognition method using histogram of oriented gradient - support vector machine (HOG-SVM) and grid search (GS) is proposed. First, the histogram of oriented gradient (HOG) is used to extract the characteristics of traffic sign. Then the grid search technique is applied to optimize the parameters of support...
The objective of the proposed work is object position estimation, in which the system, after training with examples of images including objects such as cars, should be capable of indicating accurately by coordinates. The method is different from simple object detection, since it uses the context, i.e. the whole image. The key idea is to take an approach with Relevance Vector Machine (RVM) since it...
In this work we tackle the problem of search personalization for on-line soft goods shopping. By learning what the user likes and what the user does not like, better search rankings and therefore a better overall shopping experience can be obtained. The first contribution of the work is in terms of feature selection: given the specific nature of the domain, we combine the traditional visual and text...
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...
Machine learning algorithms for large scale data are becoming more crucial in today's world. This is due to the unprecedented size of streaming data being collected by information technology. Incremental learning is considered one of the key concepts for learning from streaming data where a learned model is updated when new data becomes available in time. In this paper, we study RBF-SVM local incremental...
SVM is one of the state-of-the-art techniques for image and video classification. When multiple kernels are available, the recently introduced multiple kernel SVM (MK-SVM) learns an optimal linear combination of the kernels, providing a new method for information fusion. In this paper we study how the behaviour of MK-SVM is affected by the norm used to regularise the kernel weights to be learnt. Through...
Supervised learning requires adequately labeled training data. In this paper, we present an approach for automatic detection of outliers in image training sets using an one-class support vector machine (SVM). The image sets were downloaded from photo communities solely based on their tags. We conducted four experiments to investigate if the one-class SVM can automatically differentiate between target...
In this paper, we present a local-driven semi-supervised learning framework to propagate the labels of the training data (with multi-label) to the unlabeled data. Instead of using each datum as a vertex of graph, we encode each extracted local feature descriptor as a vertex, and then the labels for each vertex from the training data are derived based on the context among different training data, finally...
In this work, a novel method of fusing colour information in feature level is proposed considering a face verification system. For this purpose, composite kernels which have been already used in support vector machine classifier is applied within the framework of the generalised discriminant analysis (GDA) algorithm. The performance of the resulting system is evaluated using the XM2VTS face database...
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