International Journal of Pattern Recognition and Artificial Intelligence

Evolutionary prototype selection has shown its effectiveness in the past in the prototype selection domain. It improves in most of the cases the results offered by classical prototype selection algorithms but its computational cost is expensive. In this paper, we analyze the behavior of the evolutionary prototype selection strategy, considering a complexity measure for classification problems based on overlapping. In addition, we have analyzed different k values for the nearest neighbour classifier in this domain of study to see its influence on the results of PS methods. The objective consists of predicting when the evolutionary prototype selection is effective for a particular problem, based on this overlapping measure.

The nearest neighbor method is one of the most widely used pattern classification methods. However its major drawback in practice is the curse of dimensionality. In this paper, we propose a new method to alleviate this problem significantly. In this method, we attempt to cover the training patterns of each class with a number of hyperspheres. The method attempts to design hyperspheres as compact as possible, and we pose this as a quadratic optimization problem. We performed several simulation experiments, and found that the proposed approach results in considerable speed-up over the k-nearest-neighbor method while maintaining the same level of accuray. It also significantly beats other prototype classification methods (Like LVQ, RCE and CCCD) in most performance aspects.

The goal of feature selection is to search the optimal feature subset with respect to the evaluation function. Exhaustively searching all possible feature subsets requires high computational cost. The alternative suboptimal methods are more efficient and practical but they cannot promise globally optimal results. We propose a new feature selection algorithm based on distance discriminant and distribution overlapping (HFSDD) for continuous features, which overcomes the drawbacks of the exhaustive search approaches and those of the suboptimal methods. The proposed method is able to find the optimal feature subset without exhaustive search or Branch and Bound algorithm. The most difficult problem for optimal feature selection, the search problem, is converted into a feature ranking problem following rigorous theoretical proof such that the computational complexity can be greatly reduced. Since the distribution of overlapping degrees between every two classes can provide useful information for feature selection, HFSDD also takes them into account by using a new approach to estimate the overlapping degrees. In this sense, HFSDD is a distance discriminant and distribution overlapping based solution. HFSDD was compared with ReliefF and mrmrMID on ten data sets. The experimental results show that HFSDD outperforms the other methods.

In this paper, we present a system towards Indian postal automation based on pin-code and city name recognition. Here, at first, using Run Length Smoothing Approach (RLSA), non-text blocks (postal stamp, postal seal, etc.) are detected and using positional information, Destination Address Block (DAB) is identified from postal documents. Next, lines and words of the DAB are segmented. In India, the address part of a postal document may be written by a combination of two scripts: Latin (English) and a local (State/region) script. It is very difficult to identify the script by which pin-code part is written. To overcome this problem on pin-code part, we have used a two-stage artificial neural network based general scheme to recognize pin-code numbers written in any of the two scripts. To identify the script by which a word/city name is written, we propose a water reservoir concept based feature. For recognition of city names, we propose an NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the proposed digit numeral recognition module is 93.14% while that of city name recognition scheme is 86.44%.

With the proliferation of pornographic images on the Internet, it is essential to automatically detect pornographic images by analyzing image content. Most traditional detection systems are based on low-level features and generate many false positives due to images that contain large regions of skin-like colors. In this paper, we present a novel detection method based on local features, such as SIFT (Scale Invariant Feature Transform) visual words. Support Vector Machine (SVM) is used to classify images according to their multilevel representation based on visual words and the distribution of pornography-related visual words. The multilevel representation captures inter-word statistics and fuses various visual components of pornographic scenes. Experimental results demonstrate that our method outperforms traditional skin-region and human-body-model based methods, and performs well on a wide range of test data, in particular, on human-related images.

In this article, a new minimum spanning tree-based method for shape description and matching is proposed. Its properties are checked through the problem of graphical symbols recognition. Recognition invariance in front shift and multi-oriented noisy objects was studied in the context of small and low resolution binary images. The approach seems to have many desirable properties, even if the construction of graphs induces an expensive algorithmic cost. In order to reduce time computing, an alternative solution based on image compression concepts is provided. The recognition is realized in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise, with an acceptable time computing. The position with a reference approach like Zernike moments is also investigated to measure the relevance of the proposed technique.

This paper proposes an Enhanced Lipschitz Embedding based Classifier (ELEC) for the classification of multi-emotions from speech signals. ELEC adopts geodesic distance to preserve the intrinsic geometry at all scales of speech corpus, instead of Euclidean distance. Based on the minimal geodesic distance to vectors of different emotions, ELEC maps the high dimensional feature vectors into a lower space. Through analyzing the class labels of the neighbor training vectors in the compressed low space, ELEC classifies the test data into six archetypal emotional states, i.e. neutral, anger, fear, happiness, sadness and surprise. Experimental results on clear and noisy data set demonstrate that compared with the traditional methods of dimensionality reduction and classification, ELEC achieves 15% improvement on average for speaker-independent emotion recognition and 11% for speaker-dependent.

Existing clustering algorithms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on predefined parameters specified by users. These algorithms may be unsuccessful if the choice of parameters is inappropriate with respect to the data set being clustered. Most of these algorithms work very well for compact and hyper-spherical clusters. In this paper, a new hybrid clustering algorithm called Self-Partition and Self-Merging (SPSM) is proposed. The SPSM algorithm partitions the input data set into several subclusters in the first phase and, then, removes the noisy data in the second phase. In the third phase, the normal subclusters are continuously merged to form the larger clusters based on the inter-cluster distance and intra-cluster distance criteria. From the experimental results, the SPSM algorithm is very efficient to handle the noisy data set, and to cluster the data sets of arbitrary shapes of different density. Several examples for color image show the versatility of the proposed method and compare with results described in the literature for the same images. The computational complexity of the SPSM algorithm is O(N²), where N is the number of data points.

The paper presents mathematical underpinnings of the locally linear embedding technique for data dimensionality reduction. It is shown that a cogent framework for describing the method is that of optimization on a Grassmann manifold. The solution delivered by the algorithm is characterized as a constrained minimizer for a problem in which the cost function and all the constraints are defined on such a manifold. The role of the internal gauge symmetry in solving the underlying optimization problem is illuminated.

In this article, approaches of planar Euclidean Structure recovery are proposed based on the geometric and algebraic characteristics of the dual conic. Algebraic relations between the matrix representation of the dual conic and the circular point enveloped are extensively analyzed, to attain a generalized framework for the problem. The dual conic has the decomposition with consistent geometric interpretation from which the circular point envelope can be solved. From the geometric viewpoint, based on the characteristics of the common elements of dual conics (their bitangents), a stratified approach is presented to establish the solution. Experiments on both synthetic and real images are conducted to demonstrate the robustness and accuracy of the presented approach.