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For the restoration problem of shredded paper broken by shredder machines with the same marginal feature, a new method based on ant colony algorithm with classification is proposed in this paper. Firstly, shredded paper feature vector can be extracted by image space information. Secondly, the similar matrix and the marginal distance matrix are defined by the feature vector and left-right part image...
Membrane computing also called P system, seeks to discover new computational models from the study of cellular membranes. In this study, we reported our initial efforts to classify Macao visitor expenditure profile using a membrane computing approach. Specifically, we designed a novel P system including specific membrane structure and membrane rules to realize an improved k-medoids clustering algorithm...
Feature selection has become a necessary step to the analysis of high-dimensional datasets coming from several application domains (e.g., web data, document and image analysis, biological data). Though well-established methods exist to select highly discriminative features, discarding the ones that may be either redundant or irrelevant to the problem at hand, little attention has been so far given...
This thesis tries to analyze the drawbacks and shortages of the present ant-based text clustering algorithms (Z-ACTCA algorithm in short). The author attempts to improve the ant-based text clustering algorithms from the following three aspects: text similarity calculation, iterations termination condition as well as parametric adaption. Meanwhile, preprocession will be done on the two-dimensional...
In recent days, a number of face recognition and authentication mechanisms are developed in the computer vision applications. The human faces may be obstructed by other object that makes the acquisition of fully holistic image processing as a complex task. To overcome this problem, a new partial face recognition system is introduced in this paper. This work includes the preprocessing, face detection,...
Neighborhood Covering Reduction (NCR) is an effective tool to learn rules from structural data for classification. However, the existing neighborhood covering model is not robust enough. A neighborhood is constructed according to the nearest heterogeneous samples. This strategy over focuses on the boundary samples and makes the model sensitive to noise. To tackle this problem, we proposed a Rough...
Clustering is a fundamental data mining tool that aims to divide data into groups of similar items. Intuition about clustering reflects the ideal case – exact data sets endowed with flawless dissimilarity between individual instances. In practice however, these cases are in the minority, and clustering applications are typically characterized by noisy data sets with approximate pairwise dissimilarities...
A new method for feature selection based on improved maximal relevance and minimal redundancy (mRMR) is proposed in this paper. In order to describe the influence of the added features on correlation between candidate features subset and decision, the standard mRMR was improved by introducing the calculation of parameter Sig ≥ (a, B, D). The value of Sig ≥ (a, B, D) is used to determine whether a...
Clustering is an interdisciplinary-studied subject of statistical data analysis. In this study, among various types of clustering algorithms, the algorithms derived from Density Based Spatial Clustering of Applications with Noise (DBSCAN) are investigated. Although DBSCAN is the well-known density-based algorithms it has some bottlenecks. So, enhanced versions of DBSCAN are developed to provide some...
This paper presented a SIFT based multiple instance learning algorithm to deal with the problem of pose variation in the tracking process. The MIL algorithm learns weak classifiers by using instances in the positive and negative bags. Then, a strong classifier is generated by powerful weak classifiers which are selected by maximizing the inner product between the classifier and the maximum likelihood...
Exploiting both labeled and unlabeled instances of various problems seems a really promising strategy, since useful information that is contained on the latter pool of data is discarded during supervised approaches. However, the size of the unlabeled data that needs to be examined is usually extremely large and efficient algorithms should be utilized in such cases. Hidden Naive Bayes (HNB) model constitutes...
For the character of traditional clustering algorithms can only deal with low-dimensional linear data, not sensitive to high-dimensional nonlinear data sets, this paper proposes to use normalized cut (N-cut) spectral clustering algorithm to process high-dimensional data, and combines with sparse subspace clustering algorithm to generate similarity matrix, the method overcomes the insensitivity of...
Recent researches proposed that the weighted spectral distribution is a robust spectral metric independent of the network size (node number) and can be quickly calculated in large-scale networks using the graph structure of 4-cycles. In this paper, we design an algorithm for calculating the spectral metric within a more complex graph structure (i.e., 5-cycles) and two theorems are proposed to verify...
Due to the recent financial crisis, several systemic risk measures have been proposed in the literature for quantifying financial system wide distress. In this note we propose an aggregated Index for financial systemic risk measurement based on EOF and ICA analyses on the several systemic risk measures released in the recent literature. We use this index to further identify the states of the market...
The bag-of-words (BoW) model has been widely used for acoustic event classification (AEC). The performance of the BoW based AEC model is much influenced by "codebook construction" and "histogram generation". The common approaches for constructing the codebook and generating the histogram are the K-means and vector quantization encoding (VQE) respectively. However, they have some...
A novel hand gesture detection method in complex background is presented in this paper, it proposed a multi class cascade structure classification based on Gentle AdaBoost (GAB) and Weighted Linear Discriminant Analysis (wLDA). The training and testing experiments are based on the sample database established myself. Histogram of Oriented Gradient (HoG) features of one pair of blocks are extracted...
In this paper, a possibility of evaluating frame-based nonstationary pattern recognition methods by using Bhattacharrya distance is considered. Speech signal is used as a nonstationary signal and the comparative analysis is done through analyzing the natural speech, isolately spoken Serbian vowels and digits.
In this work, a robust recursive procedure based on WRLS algorithm with VFF and a quadratic classifier with sliding training data set for identification of non-stationary AR model of speech production system is proposed. Experimental analysis is done according to the results obtained in analyzing speech signal with voiced and mixed excitation segments. Presented experimental results justify that two...
A robust recursive procedure for identification of nonstationary AR speech model based on a quadratic classifier with a heuristic decision threshold is proposed and evaluated. A comparative experimental analysis is done through processing natural speech signal with voiced and mixed excitation segments. Obtained results show that the proposed robust procedure based on the quadratic classifier with...
The real-time compressive tracking algorithm, proposed by Kaihua Zhang etc. In 2012, is real-time and robust. But this algorithm may lead to object tracking losing in some complex environments, such as pose variation, illumination change, occlusion, and motion blur etc. This paper improves the compressive tracking algorithm in two aspects: (1) we propose a self-adaptive method for learning parameter...
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