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Scene recognition applications on mobile devices receive increasing attentions in recent years. Due to mobile users' real-time requirement, an accurate and efficient scene recognition system is urgent for mobile applications. In this paper, we propose a novel discriminative codeword selection method by using the ensemble extreme learning machine (ELM) algorithm for fast and accurate scene recognition...
In this paper we present an off-line signature verification and recognition system using the global, directional and grid fea-tures of signatures. Support Vector Machine (SVM) was used to verify and classify the signatures and a classification ratio of 0.95 was obtained. As the recognition of signatures repre-sents a multiclass problem SVM's one-against-all method was used. We also compare our methods...
A person's face provides a lot of information such as age, gender and identity. Faces allow humans to estimate/ classify the age of other persons just by looking at their face. Researchers who carried out work in studying the process of age classification by humans conclude that humans are not so accurate in age classification; hence the possibility of developing facial age classification methods...
Logo identification and classification have received considerable attention from both the machine learning and computer vision communities. Vehicle logo recognition (VLR) is used to recognise accurately the manufacturer of a vehicle by using its iconic logo. A VLR system in addition to license plate recognition aims to increase the confidence of vehicle monitoring systems in private environments such...
The objective of this paper is to evaluate Bag-of-Colors (BoC) descriptor for land use classification. BoC can be used either as a global or local descriptor. In this paper we present and evaluate both approaches. We analyze the influence of different parameters on classification accuracy and introduce a modification of descriptor extraction process, which significantly influences the classification...
We present an approach for on-line recognition of handwritten math symbols using adaptations of off-line features and synthetic data generation. We compare the performance of our approach using four different classification methods: AdaBoost. M1 with C4.5 decision trees, Random Forests and Support-Vector Machines with linear and Gaussian kernels. Despite the fact that timing information can be extracted...
Training a system using a small number of instances to obtain accurate recognition/classification is a crucial need in document classification domain. The one-class classification is chosen since only positive samples are available for the training. In this paper, a new one-class classification method based on symbolic representation method is proposed. Initially a set of features is extracted from...
Script identification is an important step in multi-script document analysis. As different textures present in text portion of a script are the main distinct features of the script, in this paper, we proposed a new algorithm for printed script identification based on texture analysis. Since local patterns is a unifying concept for traditional statistical and structural approaches of texture analysis,...
Research in human action recognition has advanced along multiple fronts in recent years to address various types of actions including simple, isolated actions in staged data (e.g., KTH dataset), complex actions (e.g., Hollywood dataset) and naturally occurring actions in surveillance videos (e.g, VIRAT dataset). Several techniques including those based on gradient, flow and interest-points have been...
Among ensemble learning methods, stacking with a meta-level classifier is frequently adopted to fuse the output of multiple base-level classifiers and generate a final score. Labeled data is usually split for basetraining and meta-training, so that the meta-level learning is not impacted by over-fitting of base level classifiers on their training data. We propose a novel knowledge-transfer framework...
Similar to many other pattern recognition problems, feature extraction contributes significantly to the overall performance of an off-line signature verification system. To be successful, a feature extraction technique must be tolerant to different types of variation whilst preserving essential information of input patterns. In this paper, we describe a grid-based feature extraction technique that...
The bag-of-keypoints representation started to be used as a black box providing reliable and repeatable measurements from images for a wide range of applications such as visual object recognition and texture classification. This order less bag-of-keypoints approach has the advantage of simplicity, lack of global geometry, and state-of-the-art performance in recent texture classification tasks. In...
Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because crowd scenes are always extremely cluttered. In this paper, we design a video content analysis method for fighting event recognition in crowd scene. Our method begins with four MPEG-7 descriptors: crowd kinetic energy, motion directions histogram,...
This paper presents a study on attributes reduction, comparing five discriminant analysis techniques: FisherFace, CLDA, DLDA, YLDA and KLDA. Attributes reduction has been applied to the problem of leather defect classification using four different classifiers: C4.5, kNN, Naïve Bayes and Support Vector Machines. The results of several experiments on the performance of discriminant analysis...
Clinical initial caries appear as no obvious difference to normal ones in X-ray images. Researchers attempt to set up computer-aided system to facilitate clinical initial caries diagnosis. This paper mainly made two efforts, image enhancement using watershed and modification of SVM kernel function. Experiments show that the testing accuracy of the kernel-modified SVM caries detection is improved compared...
As a basic two-class classifier, support vector machine (SVM) has been proved to perform well in image classification, which is one of the most common tasks of image processing. However, for the n-class problem in image classification, SVM treats it as n two-class problems, in this way, unclassifiable regions exist. In this paper, we introduce fuzzy support vector machine (FSVM) and define a membership...
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