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This paper proposes a simple spatial feature combined with temporal characteristics to classify human interactions from surveillance cameras, which are far from the action scene. For the first stage, data is collected from a horizontal view. Then, the history of distance between two persons is stored during time as a temporal feature called distance signature. We use Spatio-Temporal Interest Points...
Brazilian energy distribution companies need to frequently update their database of the public lighting network. Since changes on this network are frequently not reported, it is important to monitor where they are made. An electronic device is under development for making this task easier. One component of this device uses image processing and recognition techniques to identify the luminaire models...
Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost,...
This paper proposes a novel algorithm for image feature extraction and dimension reduction, namely, the bilateral two-dimensional locality preserving projections (B2DLPP). Different from the traditional LPP based approaches, B2DLPP is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage...
This paper presents Bangla numeral character recognition system using supervised locally linear embedding algorithm and support vector machine (SVM). The locally linear embedding (LLE) algorithm is an unsupervised technique proposed for nonlinear dimensionality reduction. In this paper, we describe its supervised variant (SLLE). Where class membership information is used to map overlapping high dimensional...
In this paper, we investigate an important but understudied problem, gender classification from human gaits. And we have proved the ability of using GEI (Gait Energy Image) as a representation of human gait for arbitrary view angles. Using GEI as a discriminative feature, we construct angle classifiers and gender classifiers from different approaches. Experiments show that our system achieved a good...
This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classification. This set is based on the well established Gabor feature. A circular sum of the Gabor feature elements belonging to the same scale is proposed to reduce the effect of rotation, while a slide matching of augmented scales is proposed to address the effect of scaling. The resulting feature vector...
Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some applications, for example, face recognition. In this paper, we propose an improved sparse representation based classification algorithm. Firstly, for a discriminative representation, a non-negative constraint of sparse coefficient is...
Wavelets are widely used to extract the texture features for pattern recognition applications including biometric authentication. This can be attributed to the discriminating capability of wavelet features and the availability of fast algorithms for implementing discrete wavelet transform (DWT). In most of the wavelet based palmprint applications, distribution of energies in space-frequency domain...
In this paper, we address the problem of recovering an optimal salient image descriptor transformation for image classification. Our method involves two steps. Firstly, a binary salient map is generated to specify the regions of interest for subsequent image feature extraction. To this end, an optimal cut-off value is recovered by maximising Fisher's linear discriminant separability measure so as...
The important issue in multi-class classification on support vector machines is the decision rule, which determines whether an input pattern belongs to a predicted class. To enhance the accuracy of multi-class classification, this study proposes a multi-weighted majority voting algorithm of support vector machine (SVM), and applies it to overcome complex facial security application. The proposed algorithm...
Fingerprint classification is an important indexing scheme to speed up the search of fingerprint database for efficient large-scale identification. It is still a challenging problem due to the intrinsic class ambiguity and the difficulty for poor quality fingerprints. In this paper, we presents a fingerprint classification algorithm which generate the classification rules based on the features of...
Local binary patterns (LBP) method has been successfully applied into many texture classification areas. However, the most widely used uniform local binary patterns are defined according to the general texture micro-structures, which are not optimal for some specific application. In this paper we propose a novel adaptive local binary patterns (ALBP) method, which can adaptively choose the most suitable...
Local ridge regression classifier (LRR) is an effective local face recognition method. It suppresses the influence of local changes by setting a voting RR classifier for each image region, thus has partial robustness to local changes caused by lighting, occlusions and poses. LRR uses the concatenated vector of a sub-image as its input feature, such a feature is still not sufficient to represent an...
In this paper, a novel fusion method for gender classification from gait based on multi-view video sequences is proposed. At the feature level, each human silhouette in a whole gait period is segmented into eight different components. Then at the match score level, the discrimination distance of each corresponding component under every camera-view angle is respectively weighted. The two-dimension...
Image classification problem is one of the most challenges of computer vision. In this paper, a robust image classification approach using multilevel neural networks is proposed. In this approach, each image is fixedly divided into five regions each equal to half of the original image. Then these regions are classified by the multilevel neural classifier into five categories, i.e., ??sky??, ??water??,...
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...
In this paper, a novel discriminant analysis approach using Angular Fourier transform is proposed for face recognition. As a generalization of Fourier transform, the Angular Fourier transform is an important frequency-domain analysis technique. The proposed approach combines it with discriminant analysis method. First, this approach selects appropriate value of angle parameter for discrete Angular...
Feature extraction is an important step for face recognition. The capability of feature extraction directly influences the performance of face recognition. Recently, some manifold learning algorithms have drawn much attention. Among them, neighborhood preserving projections is one of the most promising feature extraction techniques. Though NPP has been applied in many fields, it has limitations to...
Recognition techniques for printed and handwritten text in scanned documents are significantly different. In this paper we address the problem of identifying each type. We can list at least four steps: digitalization, preprocessing, feature extraction and decision or classification. A new aspect of our approach is the use of data mining techniques on the decision step. A new set of features extracted...
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