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This paper presents a ℓ2,0-norm regularization based feature selection method to analyze very high resolution remote sensing imagery. The method tackles the feature selection problem based on a ℓ2,1-norm based objective function and a ℓ2, 0-norm equality constraint. The constrained optimization problem is solved by an efficient algorithm based on augmented Lagrangian method to figure out a stable...
Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features...
Performing sentiment analysis of tweets by training a classifier is a challenging and complex task, requiring that the classifier can correctly and reliably identify the emotional polarity of a tweet. Poor data quality, due to class imbalance or mislabeled instances, may negatively impact classification performance. Ensemble learning techniques combine multiple models in an attempt to improve classification...
This work proposes a recognition system for clothing classification by computer vision. The input is an image of the type of fashion catalog where the clothes are fully exposed with models showing their faces. For the preprocessing and features extraction the Bag of Features (BoF) is employed. There are four steps in the proposed classification method: (i) the cloth in an image is identified and located,...
Detecting an illegally parked vehicle in urban scenes of traffic monitoring system becomes more complex task due to occlusions, lighting changes, and other factors. In this paper, a new framework to detect illegally parked vehicle using dual background model subtraction is presented. In our system, the adaptive background model is generated based on statistical information of pixel intensity that...
In this paper, a novel script independent scene text segmentation method is presented. The proposed method introduces an approach for character segmentation which combines the advantages of connected component analysis, stroke width analysis and graph cuts. The method was evaluated on a standard dataset, and also on a custom dataset of scene images of text from various Indian scripts. Additionally,...
Learning from large data sets that contain samples of unknown or incorrect labels becomes increasingly important. Such problems are inherent to many big data scenarios, hence there is a need for developing robust generic approaches to learning from difficult data. In this paper, we propose a new memetic algorithm that evolves samples and labels to select a training set for support vector machines...
Road safety is influenced by the accurate placement and visibility of road signs, which are maintained based on inventories of traffic signs. These inventories are created (semi-)automatically from street-level images, based on object detection and classification. These systems often neglect the present complimentary signs (subsigns), although clearly important for the meaning and validity of signs...
The proposed system comes in the context of intelligent parking lots management and presents an approach for vacant parking spots detection and localization. Our system provides a camera-based solution, which can deal with outdoor parking lots. It returns the real time states of the parking lots providing the number of available vacant places and its specific positions in order to guide the drivers...
Ranking algorithms have proven the potential for human age estimation. Currently, a common paradigm is to compare the input face with reference faces of known age to generate a ranking relation whereby the first-rank reference is exploited for labeling the input face. In this paper, we proposed a framework to improve upon the typical ranking model, called Voting system on Ranking model (VRank), by...
Goods-producing industries continuously search to improve the quality of final products. The main approach is to identify a correlation between the process settings and the quality of the final product. In this work, a three steps robust approach is presented to improve an industrial process. The first step consists in using a Support Vector machines Regression (SVR) method to build a model of the...
A fuzzy support vector machine emphasized the noise contamination locality in its first filtering stage is proposed. As a consequence to assign locally fuzzy memberships to the learning samples in the preprocessing filtering stage, the locality enhances the support vector machine, which is originally devised to learn a classifier with the global quadratic optimization, to compromisingly adapt to the...
Human hand functions range from precise-minute handling to heavy and robust movements. Remarkably, 50 percent of all hand functions are made possible by the thumb. Therefore, developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our...
Autossociative memories (AMs) are models inspired by the human brain ability to store and recall information. They should be able to retrieve a stored information upon presentation of a partial or corrupted item. An AM that projects the input onto a linear subspace is called subspace projection autoassociative memory (SPAM). The recall phase of a SPAM model is equivalent to a multi-linear regression...
Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding...
Most traditional visual tracking algorithms ignore the importance of tracking failure detection which is helpful for occlusion handling. To address this problem, a robust visual tracking algorithm combining the tracking model and the failure detection strategy is proposed for the needs of practical applications. In order to tracking the position and scale of the target with high speed and accuracy,...
A person with sleep disorder such as apnea will stop breathing for a while during sleep. If frequently occurs, sleep disorder is dangerous for health. An early step for diagnosing apnea is by classifying the sleep stages during sleep. This study explores some shallow classifiers and their feasibility applied to sleep data. Recently, a sleep stages classification system that use deep unsupervised features...
LBP (Local Binary Pattern) is a commonly used operator to extract LBPH (LBP histogram) of an image for local texture description. For gender classification, we proposed an innovative method by extracting multi-scale LBPH in DoG (Difference of Gaussian) space in this paper. Given a facial image, we firstly preprocess it meticulously to avert the local variations of images which probably be caused by...
Human computer interaction has great variety of options. Beside others the brain computer interface (BCI) using noninvasive acquisition methods gains attraction in the last years. The area is still under massive development along with advances in the hardware acquiring EEG signal and corresponding signal processing methods. In the paper we present a competitive method based on the frequency band energy...
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