Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The visual saliency detection model simulates the human visual system to perceive the scene and has been widely used in many vision tasks. With the development of acquisition technology, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. The RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. The co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of the video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discussion are conducted to provide a holistic overview of different saliency detection methods.

Review of Visual Saliency Detection With Comprehensive Information

The illegal distribution of a digital movie is a common and significant threat to the film industry. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can now be easily distributed to a global audience. A possible means of limiting this type of digital theft is digital video watermarking whereby additional information, called a watermark, is embedded in the host video. This watermark can be extracted at the decoder and used to determine whether the video content is watermarked. This paper presents a review of the digital video watermarking techniques in which their applications, challenges, and important properties are discussed, and categorizes them based on the domain in which they embed the watermark. It then provides an overview of a few emerging innovative solutions using watermarks. Protecting a 3D video by watermarking is an emerging area of research. The relevant 3D video watermarking techniques in the literature are classified based on the image-based representations of a 3D video in stereoscopic, depth-image-based rendering, and multi-view video watermarking. We discuss each technique, and then present a survey of the literature. Finally, we provide a summary of this paper and propose some future research directions.

An Overview of Digital Video Watermarking

With the rapid development of wireless communication technology, various indoor location-based services (ILBSs) have gradually penetrated into daily life. Although many other methods have been proposed to be applied to ILBS in the past decade, WiFi-based positioning techniques with a wide range of infrastructure have attracted attention in the field of wireless transmission. In this survey, the authors divide WiFi-based indoor positioning techniques into the active positioning technique and the passive positioning technique based on whether the target carries certain devices. After reviewing a large number of excellent papers in the related field, the authors make a detailed summary of these two types of positioning techniques. In addition, they also analyse the challenges and future development trends in the current technological environment.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-com.2019.1059

Survey on WiFi-based indoor positioning techniques

In the digital world of today, global security issues have given rise to video surveillance devices. Gait-based human recognition is an emerging behavioral biometric trait for intelligent surveillance monitoring because of its non-contact and non-cooperation with subjects. Other benefits of gait recognition in video surveillance are that it can be acquired at a distance and help to identify an object under low-resolution videos. This paper surveys extensively the current progress made towards vision-based human gait recognition. This paper discusses historical research that performs analysis of gait locomotion and provides information on how gait recognition can be performed. This paper describes measuring metrics that can be used to measure the performance of gait recognition model under verification and identification mode. This paper also provides an up-to-date review of existing studies on gait recognition representations (model based and model free). We also provide an extensive survey of available gait databases used in state-of-art gait recognition models, created since 1998. Furthermore, it offers insight into open research problems that help researchers to explore unripe areas in gait analysis, such as occlusion, view variations, and appearance changes in gait recognition. This paper also identifies the future perspectives in gait recognition and also outlines the proposed work.

Vision-Based Gait Recognition: A Survey

In traditional information hiding methods, the secret information is embedded into the carriers, which will inevitably leave traces of modification on the carriers In those methods, the modified images can be easily detected by some steganalysis algorithm and thus the security can not be guaranteed To address this problem, the concept of coverless information hiding is proposed However, general coverless information hiding method has a lower information hiding capacity In this paper, we propose a novel coverless information hiding method based on the average pixel values of sub-images We generate hash sequences by a hashing algorithm and realize the secret information hiding through mapping relationship In the first place, we build a dictionary and a hash array Then we map the dictionary and the hash array through mapping relationship Furthermore, we build a multi-level index structure for retrieving the stego-images efficiently The experimental results and analysis show that our method has a good performance in the capacity of information hiding, the security, the robustness to image attacks and the hiding success rate based on different image databases

A novel coverless information hiding method based on the average pixel value of the sub-images

Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance.

View-invariant gait recognition based on kinect skeleton feature

The Just Noticeable Distortion (JND) can reliably measure the perceptual strength in image watermarking, but, it remains a challenge to computationally model the process of embedding watermark without prior knowledge of the image contents. This paper proposed a novel color image watermarking scheme in discrete cosine transform (DCT) domain based on JND, which takes both orientation diversity and color complexity features into account. Firstly, two indicator was introduced which take into account the differences in the texture types and orientation diversity of the Human Visual System (HVS) in the proposed JND contrast masking (CM) processing. In addition, a novel color complexity weight from Cb-channel is used to guarantee the scheme robustness. Then, a novel JND model combined with the proposed contrast masking and color complexity is applied into quantization watermarking scheme. Compared with the state-of-the-art methods for color image watermarking, experimental results using publicly available images show that our proposed scheme is reliable and effective.

Wenbo Wan

Papers

A novel coverless information hiding method based on the average pixel value of the sub-images

View-invariant gait recognition based on kinect skeleton feature

Color image watermarking based on orientation diversity and color complexity

A comprehensive survey on robust image watermarking

Pattern complexity-based JND estimation for quantization watermarking