Jiafa Mao

Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering

Clustering is deemed one of the most difficult and challenging problems in machine learning. In this paper, we propose a multilocal search and adaptive niching-based genetic algorithm with a consensus criterion for automatic data clustering. The proposed algorithm employs three local searches of different features in a sophisticated manner to efficiently exploit the decision space. Furthermore, we develop an adaptive niching method, which can dynamically adjust its parameter value depending on the problem instance as well as the search progress, and incorporate it into the proposed algorithm. The adaptation strategy is based on a newly devised population diversity index, which can be used to promote both genetic diversity and fitness. Consequently, diverged niches of high fitness can be formed and maintained in the population, making the approach well-suited to effective exploration of the complex decision space of clustering problems. The resulting algorithm has been used to optimize a consensus clustering criterion, which is suggested with the purpose of achieving reliable solutions. To evaluate the proposed algorithm, we have conducted a series of experiments on both synthetic and real data and compared it with other reported methods. The results show that our proposed algorithm can achieve superior performance, outperforming related methods.

A method for video authenticity based on the fingerprint of scene frame

The increasing demand for copyright protection has brought extensive interest in video fingerprinting technology, which is considered as an effective way for digital content authentication. In this paper, an innovative method for video authenticity based on the fingerprint of scene frame has been proposed. Since the probability of 5 identical scene frames existing successively in different videos is extremely low, the proposed method firstly extract 5 successive different scene frames to make up the video fingerprint. This video fingerprint is then combined with the ID information of video to form meta fingerprint data. The metadata is stored in Bag-words format, leading to the saving of 75% storage space. In the process of authentication, a binary search of inverted file is applied to accelerate the matching speed. The results of simulation experiments using MATLAB show that the video authenticity method proposed in this paper achieves excellent detection performance, with the average accuracy over 98% at an average speed of 12s per video. This makes the real-time detection become possible on the Internet.

A Biometric Key Generation Method Based on Semisupervised Data Clustering

Storing biometric templates and/or encryption keys, as adopted in traditional biometrics-based authentication methods, has raised a matter of serious concern. To address such a concern, biometric key generation, which derives encryption keys directly from statistical features of biometric data, has emerged to be a promising approach. Existing methods of this approach, however, are generally unable to appropriately model user variations, making them difficult to produce consistent and discriminative keys of high entropy for authentication purposes. This paper develops a semisupervised clustering scheme, which is optimized through a niching memetic algorithm, to effectively and simultaneously model both intra- and interuser variations. The developed scheme is employed to model the user variations on both single features and feature subsets with the purpose of recovering a large number of consistent and discriminative feature elements for key generation. Moreover, the scheme is designed to output a large number of clusters, thus further assisting in producing long while consistent and discriminative keys. Based on this scheme, a biometric key generation method is finally proposed. The performance of the proposed method has been evaluated on the biometric modality of handwritten signatures and compared with existing methods. The results show that our method can deliver consistent and discriminative keys of high entropy, outperforming-related methods.

Adaptive Multisubpopulation Competition and Multiniche Crowding-Based Memetic Algorithm for Automatic Data Clustering

Automatic data clustering, whose goal is to recover the proper number of clusters as well as appropriate partitioning of data sets, is a fundamental yet challenging problem in unsupervised learning. In this paper, adaptive multisubpopulation competition (AMC) and multiniche crowding are proposed and incorporated into a memetic algorithm to tackle the problem. The AMC mechanism is developed to ensure a diverse search over solution subspaces corresponding to different numbers of clusters while allowing more promising subspaces to be more intensively searched. In this mechanism, the amount of individuals to be migrated between subpopulations is adaptively controlled according to the performance of subpopulations as well as the diversity of cluster numbers in population. Further, the migration is restricted to occur between subpopulations with relatively similar performances. Additionally, subpopulations with different performances are devised to search their corresponding subspaces with different exploration powers. The adaptive multiniche crowding scheme is designed to promote a diverse search of the subspace while allowing an efficient convergence of the corresponding subpopulation. This is achieved by dynamically adjusting parameter values of a multiniche crowding method to form and maintain diverged niches of high fitness within the subpopulation. The performance of proposed algorithm has been demonstrated through a series of experiments on both artificial and real data, and compared with existing methods. The results reveal that our proposed algorithm can achieve superior clustering performance and outperform related methods.

Research on watermarking payload under the condition of keeping JPEG image transparency

This work focuses on the problem of maximum watermarking payload under the condition of keeping image transparency. The maximum watermarking payload of JPEG images is influenced by internal factors such as the size, complexity and visual sensitivity of images, as well as external factors including embedding operators, carrier frequency bands and embedding intensity, etc. Through the construction of payload mathematical model, theoretic analysis and experimental derivation we propose an estimation method of maximum watermarking payload based on the DCT coefficients. The feasibility and efficiency of the proposed method has been demonstrated in our experiments by adopting two embedding operators and three carrier frequency bands. Our results show, in comparison with previously related work, the proposed method can be more practical.

An Adaptive Memetic Algorithm With Rank-Based Mutation for Artificial Neural Network Architecture Optimization

Designing a well-generalized architecture for artificial neural networks (ANNs) is an important task. This paper presents an adaptive memetic algorithm with a rank-based mutation, denoted as AMARM, to design ANN architectures. The proposed algorithm introduces an adaptive multi-local search mechanism to simultaneously fine-tune the number of hidden neurons and connection weights. The adaptation of the multi-local search mechanism is achieved by identifying effective local searches based on their search characteristics. Such an algorithm is distinguishable from previous evolutionary algorithm-based methods that incorporate one single local search for evolving ANN architectures. Furthermore, a rank-based mutation strategy is devised for avoiding premature convergence during evolution. The performance of the proposed algorithm has been evaluated on a number of benchmark problems and compared with related work. The results show that the AMARM can be used to design compact ANN architectures with good generalization capability, outperforming related work.

Research on realizing the 3D occlusion tracking location method of fish's school target

This paper takes the water detection and warning system as the application background, and studies on the school of fishes target in water occlusion tracking technology. In order to realize multi-target tracking and positioning, we first design a novel fish target information acquisition platform. Then, according to optical imaging, geometry, digital image and video processing etc., we systematically discuss the basic technology of occlusion tracking, investigate the matching model of the real fish occlusion, imaginary fish occlusion, and both of them concurrently, and finally realize the 3D fish target non-occlusion tracking. We use four fishes, as an example, to design various type of occlusion experiments. The experimental results show the accuracy and effectiveness of the proposed fish 3D occlusion tracking model. Compared with the previous works, the information acquisition platform we designed is more simple and effective. We design a 3D fish information acquisition platform, which can solve occlusion tracking of multiple targets.We establish the fish occlusion tracking model.We infer the matching relationship between the real point and the virtual point.

An image authentication technology based on depth residual network

ABSTRACT The traditional image authentication technique generally determines the image attribution by extracting specific features and combining the similarity calculation algorithm. Because of the selected features dimensions, characterization and other factors, the accuracy and speed of image authentication have been restricted. In this paper, Recog-Net, an end-to-end image authentication model based on convolution neural network has been proposed. Deep residual network is chosen as the features extractor. Mahalanobis distance and threshold method are used to complete the image authentication. Experiments show that the performance of the extractors features, compared with the traditional features and the features of other convolution neural network architectures, is more excellent, with a high degree of generality, recognition rate and robustness, still having these advantages even after a substantial compression. The Recog-Net for image authentication is able to accurately authenticate the images tampered with certain range.

GrabCut algorithm for dental X-ray images based on full threshold segmentation

Teeth are difficult to be destroyed due to their corrosion resistance, high melting point and hardness. Dental biometrics can therefore provide assistance in human forensic identification, especially to the unknown corpses. One of the key issue in dental based human identification is the segmentation of Dental X-ray images. In this paper, a novel segmentation algorithm has been proposed for this purpose. The proposed algorithm is based on full threshold segmentation. We first obtain the outline image set Iwholen and crown image set Icrownm of the complete target tooth. Morphological open operation is then applied to the difference images of Iwholen and Icrownm . Subsequently, the most complete target tooth image and its corresponding crown image are selected. Getting independent target tooth image I contour and its crown image I crown from these two images. Median filtering is applied to the synthetic image of I contour and I crown, and the resulted image will be used as the Mask for GrabCut to obtain the target tooth image. Experimental results show our proposed algorithm can effectively overcome the problems of uneven grayscale distribution and adhesion of adjacent crowns in dental X-ray images. It can also achieve a high segmentation accuracy and outperform related methods to be compared.

Target Depth Measurement for Machine Monocular Vision.

Most of the existing machine vision positioning technology focused on the technology of double camera geometric positioning, or use a single camera plus non-visual sensor technology for positioning. These two technologies achieve the precise positioning by increasing the amount of data and sacrificing processing speed. In this paper, a new method of target depth measurement for machine monocular vision is proposed. According to the imaging model of the camera, the imaging parameters of the camera (such as focal length, field of view, equivalent focal length, etc.), and the basic principle of analog signal to digital signal, we derived a relationship model between the target depth, field of view, equivalent focal length and the camera resolution. Under the condition of the height of the camera and the height of the target are known, the target depth analysis is carried out. The algorithm shows that our method could effectively locate the target depth.