International Journal of Pattern Recognition and Artificial Intelligence

Clustering by fast search and finding of Density Peaks (called as DPC) introduced by Alex Rodríguez and Alessandro Laio attracted much attention in the field of pattern recognition and artificial intelligence. However, DPC still has a lot of defects that are not resolved. Firstly, the local density ${\rho }_i$ of point $i$ is affected by the cutoff distance $d_c$, which can influence the clustering result, especially for small real-world cases. Secondly, the number of clusters is still found intuitively by using the decision diagram to select the cluster centers. In order to overcome these defects, this paper proposes an automatic density peaks clustering approach using DNA genetic algorithm optimized data field and Gaussian process (referred to as ADPC-DNAGA). ADPC-DNAGA can extract the optimal value of threshold with the potential entropy of data field and automatically determine the cluster centers by Gaussian method. For any data set to be clustered, the threshold can be calculated from the data set objectively rather than the empirical estimation. The proposed clustering algorithm is benchmarked on publicly available synthetic and real-world datasets which are commonly used for testing the performance of clustering algorithms. The clustering results are compared not only with that of DPC but also with that of several well-known clustering algorithms such as Affinity Propagation, DBSCAN and Spectral Cluster. The experimental results demonstrate that our proposed clustering algorithm can find the optimal cutoff distance $d_c$, to automatically identify clusters, regardless of their shape and dimension of the embedded space, and can often outperform the comparisons.

Sequence Assembly is one of the important topics in bioinformatics research. Sequence assembly algorithm has always met the problems of poor assembling precision and low efficiency. In view of these two problems, this paper designs and implements a precise assembling algorithm under the strategy of finding the source of reads based on the MapReduce (SA-BR-MR) and Eulerian path algorithm. Computational results show that SA-BR-MR is more accurate than other algorithms. At the same time, SA-BR-MR calculates 54 sequences which are randomly selected from animals, plants and microorganisms with base lengths from hundreds to tens of thousands from NCBI. All matching rates of the 54 sequences are 100%. For each species, the algorithm summarizes the range of $K$ which makes the matching rates to be 100%. In order to verify the range of $K$ value of hepatitis C virus (HCV) and related variants, the randomly selected eight HCV variants are calculated. The results verify the correctness of $K$ range of hepatitis C and related variants from NCBI. The experiment results provide the basis for sequencing of other variants of the HCV. In addition, Spark platform is a new computing platform based on memory computation, which is featured by high efficiency and suitable for iterative calculation. Therefore, this paper designs and implements sequence assembling algorithm based on the Spark platform under the strategy of finding the source of reads (SA-BR-Spark). In comparison with SA-BR-MR, SA-BR-Spark shows a superior computational speed.

Into the data mining field, frequent approximate subgraph (FAS) mining has become an important technique with a broad spectrum of real-life applications. This fact is because several real-life phenomena can be modeled by graphs. In the literature, several algorithms have been reported for mining frequent approximate patterns on simple-graph collections; however, there are applications where more complex data structures, as multi-graphs, are needed for modeling the problem. But to the best of our knowledge, there is no FAS mining algorithm designed for dealing with multi-graphs. Therefore, in this paper, a canonical form (CF) for simple-graphs is extended to allow representing multi-graphs and a state-of-the-art algorithm for FAS mining is also extended for processing multi-graph collections by using the extended CF. Our experiments over different synthetic and real-world multi-graph collections show that the proposed algorithm has a good performance in terms of runtime and scalability. Additionally, we show the usefulness of the patterns computed by our algorithm in an image classification problem where images are represented as multi-graphs.

Race identification is an essential ability for human eyes. Race classification by machine based on face image can be used in some practical application fields. Employing holistic face analysis, local feature extraction and 3D model, many race classification methods have been introduced. In this paper, we propose a novel fusion feature based on periocular region features for classifying East Asian from Caucasian. With the periocular region landmarks, we extract five local textures or geometrical features in some interesting regions which contain available discriminating race information. And then, these effective features are fused into a remarkable feature by Adaboost training. On the composed OFD-FERET face database, our method gets perfect performance on average accuracy rate. Meanwhile, we do a plenty of additional experiments to discuss the effect on the performance caused by gender, landmark detection, glasses and image size.

There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine learning task. As in most similarity learning settings, the training data are composed of a set of pairs of objects that have been labeled as similar/dissimilar. These are first used as an input to a transformation function that computes new feature vectors for each pair by using a family of distance functions in each of the available representation spaces. Then, this information is used to learn a classifier. The approach has been tested using three different repositories. Results show that the proposed method outperforms other alternative approaches in high-dimensional spaces and highlight the benefits of using multiple distances in each representation space.

In order to improve the H.264/AVC compressed video stream error resilience in wireless channel transmission, this paper presents a spatial error concealment algorithm based on adaptive edge threshold and directional weight. Firstly, this algorithm makes use of Sobel gradient operator of image edge detection to detect the edge of adjacent macro blocks; secondly, according to specific information of adjacent macro-block of damaged macro-block, it can set gradient adaptive threshold; thirdly, it makes the direction weighted interpolation to damaged macro-block with the Sobel gradient operator of image edge detection. Experiments show that the image reconstruction quality is greatly improved by using this algorithm, which has higher application value for different video sequence as compared to the traditional spatial error concealment algorithms. This algorithm not only improves the quality of image restoration, but also has higher application value.

To remove image noise without considering the noise model, a dual-tree wavelet thresholding method (CDOA-DTDWT) is proposed through noise variance optimization. Instead of building a noise model, the proposed approach using the improved chaotic drosophila optimization algorithm (CDOA), to estimate the noise variance, and the estimated noise variance is utilized to modify wavelet coefficients in shrinkage function. To verify the optimization ability of the improved CDOA, the comparisons with basic DOA, GA, PSO and VCS are performed as well. The proposed method is tested to remove addictive noise and multiplicative noise, and denoising results are compared with other representative methods, e.g. Wiener filter, median filter, discrete wavelet transform-based thresholding (DWT), and nonoptimized dual-tree wavelet transform-based thresholding (DTDWT). Moreover, CDOA-DTDWT is applied as pre-processing utilization for tracking roller of mining machine as well. The experiment and application results prove the effectiveness and superiority of the proposed method.

Agriculture robot by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. Because of color similarity, shape irregularity, and background complex, fruit identification turns to be a very difficult task and not to mention to execute pick action. Therefore, green cucumber detection within complex background is a challenging task due to all the above-mentioned problems. In this paper, a technique based on texture analysis and color analysis is proposed for detecting cucumber in greenhouse. RGB image was converted to gray-scale image and HSI image to perform algorithm, respectively. Color analysis was carried out in the first stage to remove background, such as soil, branches, and sky, while keeping green fruit pixels presented cucumbers and leaves as many as possible. In parallel, MSER and HOG were applied to texture analysis in gray-scale image. We can obtain some candidate regions by MSER to obtain the candidate including cucumber. The support vector machine is the classifier used for the identification task. In order to further remove false positives, key points were detected by a SIFT algorithm. Then, the results of color analysis and texture analysis were merged to get candidate cucumber regions. In the last stage, the mathematical morphology operation was applied to get complete cucumber.

To improve the rate stability and make a balance for different viewpoints in distributed multi-view video coding (DMVC) system, a novel symmetric DMVC (SDMVC) scheme is proposed in this paper. In the proposed scheme, every frame from all views adopts the same encoding mode and stable output rates are achieved, which are significant to improve the transmission efficiency in the channel. Both temporal and spatial correlations are exploited, in addition, a novel side information (SI) generation algorithm aiming at better exploring the correlations of proposed scheme has been proposed to obtain better performance. The simulation results show that the proposed SDMVC scheme gets a much more stable rate than the asymmetric scheme, only with neglectable bit-rate increasing. Meanwhile, the proposed SI generation algorithm significantly improves the coding performance.

Face recognition is an important aspect of the biometric surveillance system. Generally, face recognition is a type of biometric system that can identify a specific individual by analyzing and comparing patterns in the facial image. Face recognition has distinct advantage over other biometrics is noncontact process. It has a wide variety of applications in both the law enforcement and nonlaw enforcement. While using the low resolution face images, the resolution of the image gets degraded. In this paper, to enhance the performance rate for low resolution image, the fractional Bat algorithm and multi-kernel-based spherical SVM classifier is proposed. Initially, the low resolution image is converted into the high resolution images by the kernel regression method. The GWTM process is utilized for the feature extraction by the Gabor filter, wavelet transform and local binary pattern (texture descriptors). Then, the super resolution images are applied to the feature level fusion by using the fractional Bat algorithm which comprises of fractional theory and Bat algorithm. Finally, the multi-kernel-based spherical SVM classifier is introduced for the recognition of feature images. The experimental results and performance analysis evaluated by the comparison metrics are FAR, FRR and Accuracy with existing systems. Thus, the outcome of our proposed system achieves the highest accuracy of 95% based on the training data samples, stopping criterion and number of draw attempts.

The a priori signal-to-noise ratio (SNR) plays an essential role in many speech enhancement systems. Most of the existing approaches to estimate the a priori SNR only exploit the amplitude spectra while making the phase neglected. Considering the fact that incorporating phase information into a speech processing system can significantly improve the speech quality, this paper proposes a phase-sensitive decision-directed (DD) approach for the a priori SNR estimate. By representing the short-time discrete Fourier transform (STFT) signal spectra geometrically in a complex plane, the proposed approach estimates the a priori SNR using both the magnitude and phase information while making no assumptions about the phase difference between clean speech and noise spectra. Objective evaluations in terms of the spectrograms, segmental SNR, log-spectral distance (LSD) and short-time objective intelligibility (STOI) measures are presented to demonstrate the superiority of the proposed approach compared to several competitive methods at different noise conditions and input SNR levels.

Domain knowledge of hierarchical task network (HTN) usually involves logical expressions with predicates. One needs to master two different languages which are used to describe domain knowledge and implement planner. This has presented enormous challenges for most programmers who are not familiar with logical expressions. To solve the problem a method of state variable representation from the programmer’s point of view is introduced. This method has powerful expressivity and can unify the representations of domain knowledge and planning algorithm. In Pyhop a HTN planner written in Python, methods and operators are all as ordinary Python functions rather than using a special-purpose language. Pyhop uses a Python object that contains variable bindings and does not include a horn-clause inference engine for evaluating preconditions of operators and methods. By taking a simple travel-planning problem, it shows that the method is easy to understand and integrate planning into ordinary programming.

Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation.