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This paper develops a supervised discriminant technique, called margin maximum embedding discriminant (MMED), for dimensionality reduction of high-dimensional data. In graph embedding, our objective is to find a linear transform matrix to make the samples in the same class as compact as possible and the samples belong to the different classes as dispersed as possible. The proposed method effectively...
Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic programming (GP) is a powerful and flexible machine learning technique that has been successfully applied to many...
The high number of features in many machine vision applications has a major impact on the performance of machine learning algorithms. Feature selection (FS) is an avenue to dimensionality reduction. Evolutionary search techniques have been very promising in finding solutions in the exponentially growing search space of FS problems. This paper proposes a genetic programming (GP) approach to FS where...
Quantitative techniques for spatial prediction and classification in geological survey are developing rapidly. The recent applications of machine learning techniques confirm possibilities of their application in this field of research. The paper introduces Support Vector Machines, a method derived from recent achievements in the statistical learning theory, in classification of geological units based...
Trained detectors are the most popular algorithms for the detection of vehicles or pedestrians in video sequences. To speed up the processing time the trained stages build a cascade of classifiers. Thereby the classifiers become more powerful from stage to stage. The most popular classifier for real-time applications is Adaboost applied to rectangular Haar-like features. The processing time of these...
This work proposes a novel classifier-fusion scheme using learning algorithms, i.e. syntactic models, instead of the usual Bayesian or heuristic rules. Moreover, this paper complements the previous comparative studies on DaimlerChrysler Automotive Dataset, offering a set of complementary experiments using feature extractor and classifier combinations. The experimental results provide evidence of the...
In order to improve the accuracy of multi-spectra remote sensing image classification, a terrain classification method based on support vector machine is proposed. A remote sensing image classification method based on SVM algorithm of C-SVC type is introduced and emphasis is put on the study of the improved SMO algorithm. In order to improve efficiency of classification, multiple-spectra remote sensing...
This paper describes the basic principles that using adaboost arithmetic to achieve face detection, through OPENCV software, selects the expansion of the harr-like characteristics and achieves the training of face detection classifier. Finally, we analyze how the number of the classifiers affects the results of the experiments, and then we give a more appropriate general size for the samples after...
In this paper, we firstly describe improved Adaboost algorithm, and then introduce wavelet moment, which has rotation, shift, scale moment invariants and multi-resolution characteristic, and which can not only extract image local feature, but also can extract global feature. so it is more stronger to oppose noise. This paper proposes dynamic pyramid changing idea of detector scale. A cascade classifier...
Along with the explosive growth of the Web, Web image search has become a more and more popular application which helps users digest the large amount of online visual information. Previous research mainly exploits visual information between images while rarely uses the text information surrounding the images on the Web pages. In this paper, we consider the relevance feedback as a machine learning...
This paper studies the combination of multiple classifiers with a prototyped-based supervised clustering algorithm, namely SGNG, for Thai printed character recognition. The proposed classification system consists of two steps. First, the prototypes obtained by the SGNG are firstly used to roughly classify an unknown input positioning around a training dataset. Second, several classifiers, such as...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
Cell enumeration in peripheral blood smears and cell are widely applied in biological and pathological practice. Not every area in the smear is appropriate for enumeration due to severe cell clumping or sparseness arising from smear preparation. The automatic selection of good areas for cell enumeration can reduce manual labor and provide objective and consistent results. However, this has been infrequently...
In this paper we study object learning and recognition on a humanoid robot with foveated vision. The developed approach is view-based and can learn viewpoint-independent representations for object recognition. The training data is collected statistically and in an interactive way where a human instructor freely shows the object from a number of different viewpoints. The proposed system was fully implemented...
In this paper, we briefly review AdaBoost and expand on the discrete version by building weak classifiers from a pair of biased classifiers which enable the weak classifier to abstain from classifying some samples. We show that this approach turns into a 3-bin real AdaBoost approach where the bin sizes and positions are set by the bias parameters selected by the user and dynamically change with every...
In large scale applications, hundreds of new subjects may be regularly enrolled in a biometric system. To account for the variations in data distribution caused by these new enrollments, biometric systems require regular re-training which usually results in a very large computational overhead. This paper formally introduces the concept of online learning in biometrics. We demonstrate its application...
The purpose of this research is to develop a system that used to recognize image of vehicle and classified it into their classes using image processing method and artificial neural network. In the research, all the selected images are required to go through image processing technique to obtained desired data. Images are converted into data using singular value decomposition extraction method and the...
In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. We present our findings using the histogram of oriented gradients (HOG) features in combination with two variations of the AdaBoost algorithm. First, we compare stump and tree weak classifier. Next, we investigate the influence of a large training database. Furthermore, we compare the performance...
Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ2 kernels, each of which captures a different feature channel. Our features include the distribution of edges,...
We study the problem of robust pedestrian detection. A new descriptor, Pyramidal Statistics of Oriented Filtering (PSOF), is proposed for shape representation. Unlike one-scale gradient-based methods, the PSOF descriptor constructs an image pyramid and uses a Gabor filter bank to obtain multi-scale pixel-level orientation information. Then, locally normalized pyramidal statistics of these Gabor responses...
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