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

In content-based video analysis, substantial research efforts have been focused on developing techniques to detect the boundaries between two successive shots, each comprising consecutive frames filmed with a single camera act. However, there is still room for further improvement in the detection performance. With the use of sequential change detection and the help of nonparametric density estimation principles, we propose A new shot boundary detection method that can maintain not only satisfactory detection accuracy, but also consistent detection performance based on the results of various test videos.

Video segmentation based on sequential change detection

We present an efficient approach to search for and locate all occurrences of a specific object in large video volumes, given a single query example. Locations of object occurrences are returned as spatio-temporal trajectories in the 3D video volume. Despite much work on object instance search in image datasets, these methods locate the object independently in each image, therefore do not preserve the spatio-temporal consistency in consecutive video frames. This results in sub-optimal performance if directly applied to videos, as will be shown in our experiments. We propose to locate the object jointly across video frames using spatio-temporal search. The efficiency and effectiveness of the proposed approach is demonstrated on a consumer video dataset consisting of crawled YouTube videos and mobile captured consumer clips. Our method significantly improves the localized search accuracy over the baseline, which treats each frame independently. Moreover, it is able to find the top 100 object trajectories in the 5.5-hour dataset within 30 seconds.

Fast object instance search in videos from one example

A method to estimate the peak signal-to-noise ratio of an image performs more accurately and has a smaller estimation bias and variance compared to the existing methods.

Example-based quality estimation for compressed images

This paper presents an cross-scene abnormal event detection method by adopting Bag of Words (BoW) model with Spatial Pyramid Matching Kernel (SPM) cooperating with SIFT features and a SVM classifier. Different from existing abnormal event detection methods where abnormal events happened in a well-learned scene are considered and detected, we aim to detect concerned events in public where scenes can be unlearned before. Our method is motivated by the fact that the pattern of the notable events are similar and the learned models should be transferable to examine the events in other unlearned public scenes. To learn the patterns for an abnormal event, we divide the proposed method into two steps: feature coding and spatial pooling. For the feature coding step, the codebook is generated and the feature is quantized based on small patches. For the spatial pooling step, the patches are concatenating to exploit the spatial information of local regions. The intersection kernel is used to integrate with a SVM classifier. Experimental results on two benchmark databases demonstrate the efficacy of our proposed approach.

Cross-scene abnormal event detection

We propose a novel probabilistic reasoning approach to recognizing people entering and leaving a closed room in the work place or living environment. In our method people are represented using a low-level color feature, based on which optimal recognition is carried out by exploiting the temporal correlation among the sequence of observations of people. Optimality is achieved by maximizing a joint posterior probability. Experimental results are presented to show the efficacy of the proposed approach.

Yap-Peng Tan

Papers

Video segmentation based on sequential change detection

Fast object instance search in videos from one example

Example-based quality estimation for compressed images

Cross-scene abnormal event detection

A probabilistic reasoning approach to closed-room people monitoring