The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.

This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

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The Pascal Visual Object Classes (VOC) Challenge

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/ .

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.

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Scalable Person Re-identification: A Benchmark

The K-local hyperplane distance nearest neighbor (HKNN) algorithm is a local classification method which builds nonlinear decision surfaces directly in the original sample space by using local linear manifolds. Although the HKNN method has been successfully applied in several classification tasks, it is not possible to employ distance metrics other than the Euclidean distances in this scheme, which can be considered as a major limitation of the method. In this paper we formulate the HKNN method in terms of subspaces. Advantages of the subspace formulation of the method are two-fold. First, it enables us to propose a variant of the HKNN algorithm, the local discriminative common vector (LDCV) method, which is more suitable for classification tasks where classes have similar intra-class variations. Second, the HKNN method along with the proposed method can be extended to the nonlinear case based on subspace concepts. As a result of the nonlinearization process, one may utilize a wide variety of distance functions in those local classifiers. We tested the proposed methods on several classification tasks. Experimental results show that the proposed methods yield comparable or better results than the support vector machine (SVM) classifier and its local counterpart SVM-KNN.

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Local Subspace Classifiers: Linear and Nonlinear Approaches

We tackle the detection of prominent objects in images as a retrieval task: given a global image descriptor, we find the most similar images in an annotated dataset, and transfer the object bounding boxes. We refer to this approach as data driven detection (DDD), that is an alternative to sliding windows. Previous works have used similar notions but with task-independent similarities and representations, i.e. they were not tailored to the end-goal of localization. This article proposes two contributions: (i) a metric learning algorithm and (ii) a representation of images as object probability maps, that are both optimized for detection. We show experimentally that these two contributions are crucial to DDD, do not require costly additional operations, and in some cases yield comparable or better results than state-of-the-art detectors despite conceptual simplicity and increased speed. As an application of prominent object detection, we improve fine-grained categorization by precropping images with the proposed approach.

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Predicting an Object Location Using a Global Image Representation

A document processing system and method are disclosed. In the method local scores are incrementally computed for document samples, based on local features extracted from the respective sample. A global score is estimated for the document based on the local scores currently computed, i.e., on fewer than all document samples. A confidence in a decision for the estimated global score is computed. The computed confidence is based on the local scores currently computed and, optionally, the number of samples used in computing the estimated global score. A classification decision, such as a categorization or retrieval decision for the document is output, based on the estimated score when the computed confidence in the decision reaches a threshold value.

Efficient document processing system and method

In this paper, we propose a new supervised clustering algorithm, coined as the homogeneous clustering (HC), to find the number and initial locations of the hidden units in radial basis function (RBF) neural network classifiers. In contrast to the traditional clustering algorithms introduced for this goal, the proposed algorithm is a supervised procedure where the number and initial locations of the hidden units are determined based on split of the clusters having overlaps among the classes. The basic idea of the proposed approach is to create class specific homogenous clusters where the corresponding samples are closer to their mean than the means of rival clusters belonging to other class categories. We tested the proposed clustering algorithm along with the RBF network classifier on the Graz02 object database and the ORL face database. The experimental results show that the RBF network classifier performs better when it is initialized with the proposed HC algorithm than an unsupervised k-means algorithm. Moreover, our recognition results exceed the best published results on the Graz02 database and they are comparable to the best results on the ORL face database indicating that the proposed clustering algorithm initializes the hidden unit parameters successfully.

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A Supervised Clustering Algorithm for the Initialization of RBF Neural Network Classifiers

Cross-domain fashion item retrieval naturally arises when unconstrained consumer images are used to query for fashion items in a collection of high-quality photographs provided by retailers. To perform this task, approaches typically leverage both consumer and shop domains from a given dataset to learn a domain invariant representation, allowing these images of different nature to be directly compared. When consumer images are not available beforehand, such training is impossible. In this paper, we focus on this challenging and yet practical scenario, and we propose instead to leverage representations learned for cross-domain retrieval from another source dataset and to adapt them to the target dataset for this particular setting. More precisely, we bypass the lack of consumer images and directly target the more challenging meta-domain gap which occurs between consumer images and shop images, independently of their dataset. Assuming that datasets share some similar fashion items, we cluster their shop images and leverage the clusters to automatically generate pseudo-labels. Those are used to associate consumer and shop images across datasets, which in turn allows to learn meta-domain-invariant representations suitable for cross-domain retrieval in the target dataset. The features and code are available at https://github.com/vivoutlaw/UDMA.

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Diane Larlus

Papers

Local Subspace Classifiers: Linear and Nonlinear Approaches

Predicting an Object Location Using a Global Image Representation

Efficient document processing system and method

A Supervised Clustering Algorithm for the Initialization of RBF Neural Network Classifiers

Unsupervised Meta-Domain Adaptation for Fashion Retrieval