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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...
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...
In this paper, a novel spatial-temporal multi-scale method (STMSM) is proposed to solve the problem of detecting multiple moving objects on complex background. Moving objects have multi-scale features both in spatial and temporal domain. The motion salience sub-spaces determine the moving features including position, size and trajectory of each moving object, then the problem of detecting moving objects...
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...
Tracking individuals in video sequences, especially in crowded scenes, is still a challenging research topic in the area of pattern recognition and computer vision. However, current single camera tracking approaches are mostly based on visual features only. The novelty of the approach proposed in this paper is the integration of evidences from a crowd simulation algorithm into a pure vision based...
We present a feature selection method for multivariate time-series prediction. It aims to use the best sliding window size and delay for each explanatory variable, which are usually fixed. The idea is to convert the original time-series into a set of cumulative sum with different length. The combinations of cumulative sum variables obtaining nonzero weights in sparse learning algorithms represent...
This paper introduces a generic method for the accurate analysis of junctions, relying on a statistical modeling of normalized image gradients. We analyze junctions as local visual events that do not happen by chance under a background model derived from the a-contrario methodology. The method not only provides thresholds for the detection of junctions, but also enables their accurate characterization,...
Studies on human faculties of scene recognition have lead to two broad classifications of the perceived information: local and global. It has been shown that both are processed separately and combined towards final category assignment. Recently, it was suggested that accuracy of computational models for local information closely match human performance, while it is not so for current global representations...
A computational saliency model utilizing bio-inspired features for spatiotemporal saliency is presented in this paper. We first propose distributed opponent oriented energy for compact local dynamic texture description motivated by Human Vision System. Then, we integrate the derived motion characterization and a revised self-resemblance saliency framework. High effectiveness and efficiency of the...
This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is...
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