International Journal of Pattern Recognition and Artificial Intelligence

The problem of training classifiers only with target data arises in many applications where nontarget data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers. However, their application to one-class classification has been rather limited. In this paper, we present a new ensemble method based on a nonparametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the nonparametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way.

In this paper, we propose two Laplacian nonparallel hyperplane proximal classifiers (LapNPPCs) for semi-supervised and full-supervised classification problem respectively by adding manifold regularization terms. Due to the manifold regularization terms, our LapNPPCs are able to exploit the intrinsic structure of the patterns of the training set. Furthermore, our classifiers only need to solve two systems of linear equations rather than two quadratic programming (QP) problems as needed in Laplacian twin support vector machine (LapTSVM) (Z. Qi, Y. Tian and Y. Shi, Neural Netw.35 (2012) 46–53). Numerical experiments on toy and UCI benchmark datasets show that the accuracy of our LapNPPCs is comparable with other classifiers, such as the standard SVM, TWSVM and LapTSVM, etc. It is also the case that based on our LapNPPCs, some other TWSVM type classifiers with manifold regularization can be constructed by choosing different norms and loss functions to deal with semi-supervised binary and multi-class classification problems.

We propose a new K-nearest neighbor (KNN) algorithm based on a nearest-neighbor self-contained criterion (NNscKNN) by utilizing the unlabeled data information. Our algorithm incorporates other discriminant information to train KNN classifier. This new KNN scheme is also applied in a community detection algorithm for mobile-aware service: First, as the edges of networks, the social relation between mobile nodes is quantified with social network theory; second, we would construct the mobile nodes optimal path tree and calculate the similarity index of adjacent nodes; finally, the community dispersion is defined to evaluate the clustering results and measure the quality of community structure. Promising experiments on benchmarks demonstrate the effectiveness of our approach for recognition and detection tasks.

The main goal of this present paper is to develop color detection, and counting algorithm for tangerine flowers under natural lighting conditions to estimate better yield of tangerine from orchards, and each tree picture was taken from four sides. As a result, total of 1340 subimages of tangerine flowers were detected by the newly introduced algorithm from a sample of 21 tangerine trees during blooming season. A Gaussian filter was used to reduce noise and illumination adjustment as much as possible for better clarity to identify exactly the tangerine flowers. The proposed algorithm gives accurate output of tangerine flower detection by including partially/semipartially occluded tangerine flowers and its clusters. Finally, the output of yield estimation reveals that about 10% of all total tangerine flowers turned out to be tangerine.

The simultaneous recovery of three-dimensional (3D) structure from motion (SfM) for the sequences of images, is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more image. Motion analysis and 3D shape estimation based on the estimated motion is an important problem in computer vision. The correspondence problem is an important tool in SfM where in this paper a general 3D motion based on a simple rotation, tilt, roll and translation is proposed, and then is used for 3D shape estimation. The current work expands the 2D motion estimation (shift, rotation) to accommodate general 3D motion (shift, rotation, tilt and roll). The proposed work in this paper of motion estimation of moving object is based on Fourier Descriptor Transformation (FDT) analysis before and after motion. The FDT is also used to resolve the correspondence problem in a sequence of images. We test our method on several large-scale photo collections, show the efficacy of the introduced approach to improve reconstruction accuracy.

Rapid increase in video databases has forced the industry to have efficient and effective frameworks for video retrieval and indexing. Video segmentation into scenes is widely used for video summarization, partitioning, indexing and retrieval. In this paper, we propose a framework for scene detection mainly based on entropy and Speeded Up Robust Features (SURF) features. First, we detect the fade and abrupt boundaries based on frame entropy analysis and SURF features matching. Fade boundaries are smart indication of scenes beginning or ending in many videos and dramas, and are detected by frame entropy analysis. Before abrupt boundary detection, unnecessary frames which are obviously not abrupt boundaries, such as blank screens, high intensity influenced images, sliding credits, are removed. Candidate boundaries are detected to make SURF features efficient for abrupt boundary detection, and SURF features between candidate boundaries and their adjacent frames are used to detect the abrupt boundaries. Second, key frames are extracted from abrupt shots. We evaluate our key frame extraction with other famous algorithms and show the effectiveness of the key frames. Finally, scene boundaries are detected using sliding window of size K over the key frames in temporal order. In experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm for shot boundary achieves substantial improvements over state-of-the-art methods with the precision of 99% and the recall of 97.8%. Experimental results for video segmentation into scenes are also promising, compared to famous state-of-the-art techniques.

This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems.

The intra-subject variation of a captured image under different style conditions can be much larger than the inter-subjects variation. This fact highly affects the recognition rate. Among approaches in the literature, bilinear models are efficient two-factor models that separate observations into two factors namely style and content. This paper proposes a new incremental robust face recognition method based on separating the identity factor and the style factor using a symmetric bilinear approach. The proposed method uses an iterative orthogonal triangular factorization for bilinear model learning, along with a translation procedure of the latter. This translation requires a repetitive computation of the orthogonal triangular factorization to reach the content and style factors of an unknown subject. We perform experiments with six databases in terms of recognition rate and style accuracy under four different styles: variety of illuminations, head positions, face expressions and occlusions. Experimental results show that our approach achieves better performance in terms of recognition rate than other existing methods such as the bilinear model, the ridge regression bilinear model and the Robust Principal Component Analysis.

This paper presents a new method for multiple fuzzy rules interpolation with weighted antecedent variables in sparse fuzzy rule-based systems based on polygonal membership functions. First, the proposed method calculates the normalized weighting vector of each closest fuzzy rule. Then, it calculates the composite weight of each closest fuzzy rule. Then, it calculates the left normal point and the right normal point of the fuzzy interpolative reasoning result , respectively. Finally, it calculates the characteristic points and of the fuzzy interpolative reasoning result B*, respectively. The experimental results show that the proposed method can generate more reasonable fuzzy interpolative reasoning results than the existing methods for sparse fuzzy rule-based systems. The proposed method can overcome the drawbacks of Chang etal.'s method (IEEE Trans. Fuzzy Syst.16(5) (2008) 1285–1301), Chen and Ko's method (IEEE Trans. Fuzzy Syst.16(6) (2008) 1626–1648) and Huang and Shen's method (IEEE Trans. Fuzzy Syst.14(2) (2006) 340–359) for multiple fuzzy rules interpolation. It provides us with a useful way for dealing with multiple fuzzy rules interpolation in sparse fuzzy rule-based systems.

Navigation has been a major challenge for the successful operation of an autonomous aircraft. Although success has been achieved using active methods such as radar, sonar, lidar and the global positioning system (GPS), such methods are not always suitable due to their susceptibility to jamming and outages. Vision, as a passive navigation method, is considered as an excellent alternative; however, the development of vision-based autonomous systems for outdoor environments has proven difficult. For flying systems, this is compounded by the additional challenges posed by environmental and atmospheric conditions. In this paper, we present a novel passive vision-based algorithm which is invariant to illumination, scale and rotation. We use a three stage landmark recognition algorithm and an algorithm for waypoint matching. Our algorithms have been tested in both synthetic and real-world outdoor environments demonstrating overall good performance. We further compare our feature matching method with the speed-up robust features (SURF) method with results demonstrating that our method outperforms the SURF method in feature matching as well as computational cost.