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This paper delves into the effectiveness of a gait recognition process depending on the length of the video sequence used. To this end, a well-known gait representation, the Gait Energy Image (GEI), is incrementally computed from gait cycles in the order they occur. The main objective is to assess the problem of the minimum number of gait cycles required to obtain discriminant GEIs. An experimental...
In the dissimilarity representation approach, objects are represented by their dissimilarities with respect to a representation set, rather than by features. Up to now, the representation or prototype set has usually been selected from the training data, limiting the different aspects that can be captured, especially when the training data set is small. This paper studies the performance change if...
Recently the improved bag of features (BoF) model with locality-constrained linear coding (LLC) and spatial pyramid matching (SPM) achieved state-of-the-art performance in image classification. However, only adopting SPM to exploit spatial information is not enough for satisfactory performance. In this paper, we use hierarchical temporal memory (HTM) cortical learning algorithms to extend this LLC...
Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
This paper explores the classification problem based on parallel feature partitioning. This formulation leads to a new problem in computational geometry. While this new problem appears to be NP-complete, it is shown that the proposed graph theoretical platform makes it semi-tractable, allowing the use of conventional tools for its solution. Here, by conventional, we mean any exact or heuristic algorithm...
Insect species recognition is more difficult than generic object recognition because of the similarity between different species. In this paper, we propose a hybrid approach called discriminative local soft coding (DLSoft) which combines local and discriminative coding strategies together. Our method takes use of neighbor codewords to get a local soft coding and class specific codebooks (sets of codewords)...
Spatial pyramid matching (SPM) component is part of most state-of-art image classification methods. SPM encodes spatial distribution of image features, in an un-supervised fashion, by partitioning an image into regions at multiple scales and concatenating feature vectors for these regions. In this paper we propose to replace the unsupervised SPM procedure with a supervised two-stage feature selection...
In a pattern recognition sequence consisting of alternating steps of interactive labeling, classifier training, and automated labeling (e.g., CAVIAR systems), the choice of sample size at each step affects the overall amount of human interaction necessary to label all the samples correctly. The appropriate splits depend on the error rate of the classifier as a function of the size of the training...
Most existing methods for action recognition mainly rely on manually engineered features which, despite their good performances, are highly problem dependent. We propose in this paper a fully automated model, which learns to classify human actions without using any prior knowledge. A convolutional sparse autoencoder learns to extract sparse shift-invariant representations of the 2D local patterns...
Vein image recognition based on modeling shape or geometrical layout of feature points is generative approach, and the performance is usually limited by segmentation error due to poor vein image quality. This paper instead proposes to model the discriminative appearance of local image patch using the vocabulary tree model. The discriminative approach is further extended to consider the geometrical...
The mathematical modeling of classifier has been intensively investigated in pattern recognition for decades. Maximin classifier, which conducts optimization based on the perpendicularly closest data point(s) to the decision boundary, has been widely used. However, such method may lead to inferior performance when the boundary data point(s) is significantly influenced by noise. This paper presents...
We classify digits of real-world house numbers using convolutional neural networks (ConvNets). Con-vNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning...
Combining several binary image operators, each one based on different windows, has proven to be an effective way to produce operators with better performance than designing single operators based on one window only. To facilitate the combination task that so far is done manually, we propose a genetic algorithm (GA) based approach. It consists of the definition of a collection of candidate windows...
We present a systematic approach to reduce the dimensionality of the feature vector for local binary/ternary patterns. The proposed framework examines the distribution of uniform patterns in different image sets to formulate a procedure to assign dimensionality to uniform and non-uniform patterns. Unlike previous methods where all the information from non-uniform patterns is discarded or merged into...
Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From...
Finding discriminant features is useful for pattern recognition applications. In this work, geometric matching is combined with linear discriminant analysis (LDA) to learn the importance of the features of symbols, and assign weights to these features accordingly. The features are the line segments of the symbols. We use geometric matching within a symbol spotting system to get information on the...
Person re-identification is an important problem in visual surveillance where appearance plays a key role. Color is one of the widely used appearance features and utilizing more color spaces doesn't imply benefit of performance enhancement. That's because the poor performance color spaces influence on the high ones. So it is significant to evaluate the performance of different color spaces for person...
This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences...
In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training.
In this paper, we propose a novel unsupervised online learning trajectory analysis method based on weighted directed graph. Each trajectory can be represented as a sequence of key points. In the training stage, unsupervised expectation-maximization algorithm (EM) is applied for training data to cluster key points. Each class is a Gaussian distribution. It is considered as a node of the graph. According...
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