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 in this paper a novel subspace learning method called tensor neighborhood preserving discriminant projections (TNPDP) for face recognition. Compared with the conventional appearance-based face recognition method, the proposed TNPDP does not need to perform image-to-vector conversion and can well preserve the structure of the original image. Different from the existing tensor-based recognition approaches such as tensor subspace analysis (TSA) and discriminant analysis with tensor representation (DATER), TNPDP considers locality and discriminative information simultaneously and can find the optimal tensor subspace that best maintains locality neighborhood manifold and discriminates different classes by maximizing the between-class scatter while minimizing the within-class scatter. Experimental results on two benchmark face databases demonstrate the effectiveness of the proposed method and indicate that TNPDP is better than TSA and DATER, as well as other popular face recognition methods such as principal component analysis (PCA) and linear discrimination analysis (LDA).

Enhanced face recognition using tensor neighborhood preserving discriminant projections

We propose in this paper an efficient, adaptive, and locality-based graph partitioning method for video scene clustering. First, a graph partitioning method is proposed to group video shots into scenes, and a peer-group filtering (PGF) scheme is used to identify all the shots similar to each particular shot based on Fisher's discriminant analysis. To work with computable shot similarity measures that have only limited discriminating power, we develop a graph partitioning scheme to cluster the shots by maximizing the likeness of shots within the same cluster and minimizing that between different clusters. Second, considering that video data are normally obtained and viewed sequentially, we propose to perform a locality-based PGF and graph partitioning on video segments with 50 shots, 100 shots, and so on. This proposed locality-based method has the advantage that the number of scene clusters is not required to be known a priori, and it can achieve performance comparable to that processing on the whole video sequence. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed method.

Real-Time, Adaptive, and Locality-Based Graph Partitioning Method for Video Scene Clustering

This paper presents a new method to interpolate the color filter array (CFA) pattern that is commonly used in a single-sensor digital camera. The proposed method involves two main steps: slicing the color planes into layers to identify smooth local image regions; estimating the missing color values based on the spectral correlation between color planes. The performance of the proposed algorithm is evaluated by comparing the results with those generated by other existing methods. It shows that the proposed method outperforms other methods in terms of both subjective and objective image quality.

Layering-based color filter array interpolation

We propose in this paper a multilinear locality preserving canonical correlation analysis (MLPCCA) method for face recognition. Motivated by the fact that both spatial structure information within each face sample and local geometry information among multiple face samples are useful for facial image feature extraction, we utilize them simultaneously and derive an improved canonical correlation analysis algorithm — MLPCCA — to seek multiple sets of pairwise projection bases to maximize the correlation of two facial image sets. The proposed MLPCCA method is designed to characterize the potential nonlinear correlation of two image sets by utilizing both the spatial and local geometrical information, hence is more suitable for face recognition across large pose and illumination variants. Experimental results are presented to demonstrate the efficacy of the proposed method.

Multilinear locality preserving canonical correlation analysis for face recognition

We propose a graph-based optimization framework to leverage category independent object proposals (candidate object regions) for logo search in a large scale image database. The proposed contour-based feature descriptor EdgeBoW is robust to view-angle changes, varying illumination conditions and can implicitly capture the significant object shape information. Having been equipped with a local descriptor, it can handle a fair amount of occlusion and deformation frequently present in a real-life scenario. Given a small set of initially retrieved candidate object proposals, a fast graph-based short-listing scheme is designed to exploit the mutual similarities among these proposals for eliminating outliers. In contrast to a coarse image-level pairwise similarity measure, this search focussed on a few specific image regions provides a more accurate method for matching. The proposed query expansion strategy aims to assess each of the remaining better matched proposals against all its neighbors within the same image for a precise localization. Combined with an efficient feature descriptor EdgeBoW, a set of more insightful edge-weights and node-utility measures can yield promising results, specially for object categories primarily defined by its shape. Extensive set of experiments performed on a number of benchmark datasets demonstrates its effectiveness and superior generalization ability in both clutter intensive real-life images and poor quality binary document images.

Yap-Peng Tan

Papers

Enhanced face recognition using tensor neighborhood preserving discriminant projections

Real-Time, Adaptive, and Locality-Based Graph Partitioning Method for Video Scene Clustering

Layering-based color filter array interpolation

Multilinear locality preserving canonical correlation analysis for face recognition

Query-Adaptive Logo Search using Shape-Aware Descriptors