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

For many practical face recognition systems such as law enforcement, e-passport, and ID card identification, there is usually only a single sample per person (SSPP) enrolled in these systems, and many existing face recognition methods may fail to work well because there are not enough samples for discriminative feature extraction in this scenario. However, the probe samples of these face recognition systems are usually captured on the spot, and it is possible to collect multiple face images per person for on-location probing, which is potentially useful to improve the recognition performance. In this paper, we propose a method based on locality repulsion projections (LRP) and a sparse reconstruction-based similarity measure (SRSM) to address the problem of SSPP face recognition using multiple probe images. The LRP method is motivated by our observation that similar face images from different people may lie in a locality in the feature space and cause misclassifications. We design the method with the aim of separating the samples of different classes within a neighborhood through subspace projections for easier classification. To better characterize the similarity between each gallery face and the probe image set, we propose a SRSM method for assigning a label to each probe image set. Experimental results on five widely used face datasets are presented to demonstrate the effectiveness of the proposed approach.

Image-to-Set Face Recognition Using Locality Repulsion Projections and Sparse Reconstruction-Based Similarity Measure

In this paper, we propose a multi-feature ordinal ranking (MFOR) method for facial age estimation. Different from most existing facial age estimation approaches where age estimation is treated as a classification or a regression problem, we formulate facial age estimation as a group of ordinal ranking subproblems, and each subproblem derives a separating hyperplane to divide face instances into two groups: samples with age larger than k and samples with labels no larger than k. To better extract complementary information from different facial features, we construct multiple ordinal ranking models, each corresponding to a feature set, and aggregate them into an effective age estimator. Experimental results on two public face aging datasets are presented to demonstrate the efficacy of the proposed method.

Multi-feature ordinal ranking for facial age estimation

Downsizing video transcoding is a technique for adapting the bit rate or frame size of a preceded video to suit better the constraints of transmission networks and target applications. To sustain good video quality at reduced bit rates, we have previously proposed H.263-based transcoding techniques for downsizing preceded videos by an arbitrary factor. To fully exploit the advantages of video transcoding, we investigate and propose in this paper an effective and fast motion re-estimation method for arbitrary downsizing video transcoding using the newly developed H.264/AVC standard. Specifically, as H.264/AVC allows more macroblock partitions and reference frames for inter-frame motion-compensated prediction, we propose an area-weighted vector median filter to estimate the new required motion vectors from the motion vectors existing in a preceded video. Experimental results show that the proposed method can achieve a notable improvement in both subjective and objective video quality for transcoding preceded H.263 or H.264 videos at reduced bit rates and frame sizes.

Fast motion re-estimation for arbitrary downsizing video transcoding using H.264/AVC standard

Video contents are inherently heterogeneous. To exploit different feature modalities in a diverse video collection for video summarization, we propose to formulate the task as a multiview representative selection problem. The goal is to select visual elements that are representative of a video consistently across different views (i.e., feature modalities). We present in this paper the multiview sparse dictionary selection with centroid co-regularization method, which optimizes the representative selection in each view, and enforces that the view-specific selections to be similar by regularizing them towards a consensus selection. We also introduce a diversity regularizer to favor a selection of diverse representatives. The problem can be efficiently solved by an alternating minimizing optimization with the fast iterative shrinkage thresholding algorithm. Experiments on synthetic data and benchmark video datasets validate the effectiveness of the proposed approach for video summarization, in comparison with other video summarization methods and representative selection methods such as K-medoids, sparse dictionary selection, and multiview clustering.

Video Summarization Via Multiview Representative Selection.

In this paper, we propose a scalable resource allocation framework for streaming scalable videos over multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) networks. We exploit the utilities of scalable videos produced by the scalable extension of H.264/AVC (SVC) and investigate the multidimensional diversities of the multiuser MIMO-OFDM wireless networks. First, we study the rate-utility relationship of SVC via a packet prioritization scheme. Based on the rate-utility analysis, a scalable resource-allocation framework is proposed to achieve differentiated service objectives for different scalable video layers. To provide users with fair opportunities to acquire basic viewing experience, a fair scheme is designed to guarantee that each user is entitled to a MAXMIN fairness to have their base layer video packets received. After all users have their base layer packets successfully scheduled, resources are distributed to exploit the network efficiency. The two schemes are integrated into a unified bit loading and power allocation solution to enhance the practicability of the scalable framework. Experiment results confirms that the proposed scheme handles fairness and efficiency better at different scenarios than the conventional schemes.

Yap-Peng Tan

Papers

Image-to-Set Face Recognition Using Locality Repulsion Projections and Sparse Reconstruction-Based Similarity Measure

Multi-feature ordinal ranking for facial age estimation

Fast motion re-estimation for arbitrary downsizing video transcoding using H.264/AVC standard

Video Summarization Via Multiview Representative Selection.

Scalable Resource Allocation for SVC Video Streaming Over Multiuser MIMO-OFDM Networks