International Journal of Pattern Recognition and Artificial Intelligence

Every year, agriculture experience significant economic loss due to wild geese, rooks and other flocks of birds. A wide range of devices to detect and deter animals causing conflict is used to prevent this, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviors could form a critical component of a system capable of altering the disruptive stimulus to avoid habituation. This paper presents an audio-visual-based approach for recognition of goose flocking behavior. The vocal communication and movement of the flock is used for the audio-visual recognition, which is accomplished through classifier fusion of an acoustic and a video-based classifier. Acoustic behavior recognition is based on generalized perceptual features and support vector machines, and visual behavior recognition is based on optical flow estimation and a Bayesian Rule-Based scheme. Classifier fusion is implemented using the product rule on the soft-outputs from both classifiers. The algorithm has been used to recognize goose flocking behaviors (landing, foraging and flushing) and have improved the performance compared to using audio- or video-based classifiers alone. The improvement of using classifier fusion is most evident in the case of flushing and landing behavior recognition, where it was possible to combine the advantages of both the audio- and video-based classifier.

In this paper, we propose a novel similarity measure between intuitionistic fuzzy sets and apply it to deal with pattern recognition problems and medical diagnosis problems. First, we propose a new similarity measure between intuitionistic fuzzy values based on the medians of intervals, the Hausdorff distance, and the ratio of the uncertainty degrees of intuitionistic fuzzy values. We also prove some properties of the proposed similarity measure between intuitionistic fuzzy values. Then, based on the proposed similarity measure between intuitionistic fuzzy values, we propose a novel similarity measure between intuitionistic fuzzy sets. It can overcome the drawbacks of existing methods for measuring the degree of similarity between intuitionistic fuzzy sets. We also prove some properties of the proposed similarity measure between intuitionistic fuzzy sets. Finally, we apply the proposed similarity measure between intuitionistic fuzzy sets to deal with pattern recognition problems and medical diagnosis problems.

In semi-supervised learning, when the number of data samples with class label information is very small, information from unlabeled data is utilized in the learning process. Many semi-supervised learning methods have been presented and have exhibited competent performance. Active learning also aims to overcome the shortage of labeled data by obtaining class labels for some selected unlabeled data from experts. However, the selection process for the most informative unlabeled data samples can be demanding when the search is performed over a large set of unlabeled data. In this paper, we propose a method for batch mode active learning in graph-based semi-supervised learning. Instead of acquiring class label information of one unlabeled data sample at a time, we obtain information about several data samples at once, reducing time complexity while preserving the beneficial effects of active learning. Experimental results demonstrate the improved performance of the proposed method.

Leave-one-out (LOO) and its generalization, K-Fold, are among most well-known cross-validation methods, which divide the sample into many folds, each one of which is, in turn, left out for testing, while the other parts are used for training. In this study, as an extension of this idea, we propose a new cross-validation approach that we called miss-one-out (MOO) that mislabels the example(s) in each fold and keeps this fold in the training set as well, rather than leaving it out as LOO does. Then, MOO tests whether the trained classifier can correct the erroneous label of the training sample. In principle, having only one fold deliberately labeled incorrectly should have only a small effect on the classifier that uses this bad-fold along with K - 1 good folds and can be utilized as a generalization measure of the classifier. Experimental results on a number of benchmark datasets and three real bioinformatics dataset show that MOO can better estimate the test set accuracy of the classifier.

Multi-label learning (MLL) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLL are the large-scale problem, which have a strong impact on the computational complexity of learning. These problems are especially pronounced for approaches that transform MLL problems into a set of binary classification problems for which Support Vector Machines (SVMs) are used. On the other hand, the most efficient approaches to MLL, based on decision trees, have clearly lower predictive performance. We propose a hybrid decision tree architecture, where the leaves do not give multi-label predictions directly, but rather utilize local SVM-based classifiers giving multi-label predictions. A binary relevance architecture is employed in the leaves, where a binary SVM classifier is built for each of the labels relevant to that particular leaf. We use a broad range of multi-label datasets with a variety of evaluation measures to evaluate the proposed method against related and state-of-the-art methods, both in terms of predictive performance and time complexity. Our hybrid architecture on almost every large classification problem outperforms the competing approaches in terms of the predictive performance, while its computational efficiency is significantly improved as a result of the integrated decision tree.

Skew correction of a scanned document page is an important preprocessing step in document image analysis. We propose here a fast and robust skew estimation algorithm based on rank analysis in Farey sequence. Our target document class comprises two major Indian scripts with headlines, namely Devnagari and Bangla. At the beginning, straight edge segments from the edge map of the document page are detected by our algorithm using properties of digital straightness. Straight edges derived in this manner are binned by Farey ranks in correspondence with their slopes. The principal bin, identified from these bins using the strength of accumulated edge points, represents the principal direction along the direction of headlines, from which the gross skew angle is estimated. A fast refinement algorithm is then applied with a finer tuning of Farey ranks, to detect the skew up to the desired level of precision. The algorithm has been tested on a diverse set of document images, containing Bangla and Devnagari scripts. Experimental results are quite encouraging in terms of accuracy, sensitivity to non-textual objects, effectiveness in dealing with unrestricted layouts, and computational efficiency.

Optical Music Recognition (OMR) is a technique for converting printed musical documents into computer readable formats. In this paper, we present a simple OMR system that can perform well for ordinary musical documents such as ballad and pop music. This system is constructed based on fundamental image processing and pattern recognition techniques, thus it is easy to implement. Moreover, this system has a strong capability in skew restoration and inverted musical score detection. From a series of experiments, the error for our skew restoration is below 0.2° for any possible document rotation and the accuracy of inverted musical score detection is up to 98.89%. The overall recognition accuracy of our OMR can achieve to nearly 97%, a figure comparable with current commercial OMR software. However, if taking into image skew into consideration, our system is superior to commercial software in terms of recognition accuracy.

Curve matching plays an important role in pattern recognition, computer vision and image understanding. In several past years, this problem has been studied mainly based on the curve contour, while only little progress has been made using the texture feature of the curve's neighborhood. This paper develops a novel texture-based curve matching method called IOCD, which consists of three steps: (1) Curve support region (CSR) without assigning a dominant orientation is first determined; (2) CSR is equally partitioned into several order bins according to the overall intensity order; (3) The feature vector is computed based on the local intensity order mapping. Experiments prove that the proposed IOCD performs robust to image rotation, viewpoint change, illumination change, blur, noise and JPEG compress. The application of image mosaic further identifies IOCD can achieve good matching performance.

Pedestrian segmentation and pose tracking are performed to infer human silhouettes and skeletons, respectively. Although the two tasks are complementary in nature, few works have been done on combining them together to improve each other, and some related methods are limited to still images. In this paper, we propose an approach to jointly solving them in monocular videos via a unified framework. Basically, the framework is built on EM-based maximum likelihood estimation, in which pose tracking is fulfilled through Bayesian filtering using body silhouette as an observation cue, and pedestrian segmentation is inferred by guided filtering with constraint of body skeleton. The two sets of parameters are alternatively updated along the video. In the initialization of the framework, we utilize a hierarchical shape matching scheme to obtain the silhouette and skeleton in the first frame. Experiments on challenging pedestrian datasets verify the approach's effectiveness to cluttered backgrounds, moving camera and various articulated bodies, and the performance is improved significantly by solving the two tasks together.

The problem of divisible load scheduling in network based heterogeneous distributed systems is addressed in this paper, where a general platform is considered, and the communication is in non-blocking message receiving mode, moreover, the communication speeds, computation speeds, start-up overheads and workload size are arbitrary. To solve the problem efficiently, we set up an optimization model which can effectively tackle the following three issues: (1) how many and which processors are required in computation; (2) in which order the load fractions are distributed to processors; (3) how much the load fraction should be distributed to each processor. For this model, a novel genetic algorithm is proposed, and the convergence of the proposed algorithm to a globally optimal solution with probability one is proved. Finally, the experiments on several examples indicate the efficiency and effectiveness of the proposed algorithm.