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

Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter-class dispension and intra-class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state-of-the-art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons), significantly improving the previous results and setting new state-of-the-art for both face recognition and face verification tasks.

/pdf/a-discriminative-feature-learning-approach-for-deep-face-2zrw7nm451.pdf

A Discriminative Feature Learning Approach for Deep Face Recognition

The PASCAL Visual Object Classes Challenge

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks.

/pdf/sphereface-deep-hypersphere-embedding-for-face-recognition-bof1bk9snh.pdf

SphereFace: Deep Hypersphere Embedding for Face Recognition

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

We propose a video transcoding method for arbitrarily downsizing a precoded video by reestimating from its original motion vectors the new motion vectors required to code the downsized video. Compared with the existing methods for downsizing a precoded video by an integral factor, the main advantage of the proposed method is that the spatial resolution of the precoded video can be freely adjusted to meet different bandwidth and device requirements. Experimental results show that the proposed method can obtain arbitrarily downsized video with good perceptual quality while reducing the computational complexity of the process.

Arbitrary downsizing video transcoding using fast motion vector reestimation

Color filter array (CFA) demosaicking is an essential process for restoring full-color images from incomplete color samples acquired by single-sensor digital cameras. We present two main contributions to CFA demosaicking. First, we analyze the causes of two main types of CFA demosaicking artifacts and examine the schemes that are effective in suppressing them, respectively. Second, by combining and extending the core merits of the schemes examined, we compose a new CFA demosaicking algorithm to suppress as many demosaicking artifacts as possible and obtain full-color images of high quality. Experiments using a large variety of test images have been conducted, and the results show that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio.

Hybrid color filter array demosaicking for effective artifact suppression

In content-based video analysis, shot boundary detection (SBD) is a common first step which segments video data into elementary shots, each comprising a sequence of consecutive frames recording a video event or scene continuous in time and space. Many SBD methods have been proposed in the literature, and experimental results show that the existing methods work reasonably well for abrupt shot boundaries, but less effectively for gradual shot boundaries. In this paper, we propose an effective post-refinement method for identifying actual shot boundaries from the results obtained by existing SBD methods. The proposed method formulates the SBD problem as sequential detection of changes in the underlying feature distributions whose parameters are estimated from existing video shots. Specifically, the proposed post-refinement method enhances the performance of SBD by identifying as many false positives (false detections) and false negatives (miss detections) as possible. Experiments conducted on a large set of test videos, whose initial shot boundaries are obtained by four existing SBD methods, show that the proposed post-refinement method can improve markedly the detection recall and precision and is rather insensitive to the thresholds used by the existing methods in detecting the initial shot boundaries.

An effective post-refinement method for shot boundary detection

We propose in this letter two new subspace learning methods, called nearest feature space analysis (NFSA) and discriminant nearest feature space analysis (DNFSA), for pattern classification. While many subspace learning algorithms have been proposed in recent years, most of them apply the conventional nearest neighbor (NN) metric to derive the subspace and may not effectively characterize the geometrical information of the samples, especially when the number of training samples per class is limited. In this paper, we propose using the nearest feature space (NFS) metric to seek a NFSA subspace to improve the discriminating power of the subspace for classification. To further enhance the discriminative power of NFSA, we also propose a new DNFSA method to minimize the within-class feature space (FS) distances and maximize the between-class FS distances simultaneously in the derived subspace. Experimental results on face and facial expression recognition are presented to demonstrate the efficacy of the proposed methods.

Nearest Feature Space Analysis for Classification

Luminance information plays an important role in dictating the quality of a color image, and some existing color filter array (CFA) demosaicking methods achieve superiority by first reconstructing a satisfactory luminance plane. It is however difficult to accurately estimate the luminance from CFA samples since their color spectra are generally aliased. Extending a state-of-the-art luminance-based demosaicking scheme, in this paper we propose an improved demosaicking method using an efficient filter to estimate the luminance at green pixels and employing an effective edge-adaptive interpolation scheme to obtain the luminance at red and blue pixels. Experimental results demonstrate that not only is the proposed method less complex, it also performs noticeably better, both visually and in terms of peak signal-to-noise ratio, comparing with several recent methods.

Yap-Peng Tan

Papers

Arbitrary downsizing video transcoding using fast motion vector reestimation

Hybrid color filter array demosaicking for effective artifact suppression

An effective post-refinement method for shot boundary detection

Nearest Feature Space Analysis for Classification

Improved color filter array demosaicking by accurate luminance estimation