This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name

Visual content analysis and understanding attract tremendous attention because of its potentially wide range of applications including human activity analysis, automated photo face tagging, multicamera tracking, crowded counting, and biometric security. With recent progress in end-to-end differentiable learning, the accuracy of algorithms has been significantly improved and even outperforms humans

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Although video enhancement and evaluation have been studied for many years, they are still challenging due to the evolutions of video acquiring and processing techniques. While the development of deep learning is undoubtedly exciting and has demonstrated its superior performance in a variety of applications, it is important to further investigate advanced technologies and solutions to bring seemingly

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

In Recent years, with the vast development of deep learning techniques, a great deal of effort has been devoted in the computer vision and multimedia community toward the problems of visual object analysis, such as object representation, recognition, detection, identification, etc. Especially at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014, the computers have successfully

Presents the table of contents for this issue of the publication.

Representation learning methods allow a system to automatically learn robust and discriminative features from raw data for given goals, which play an important role in various visual content understanding applications, such as visual object segmentation, detection, tracking, recognition, and search. The performance of visual content understanding tasks is heavily dependent on the choice of data representation

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The rapid development and growing of 4G and 5G mobile networks allow people all over the world to efficiently transmit and share data and information while bringing an increasing demand on information security. Data hiding is a general technique to embed secret messages to be protected in an imperceptible way into a cover media like an image, a video stream, or a document. Traditional data hiding intends

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Video is being watched more than ever before. It is estimated that in 2020, 82% of global IP traffic and 79% of global Internet traffic will come from video; globally 3 trillion minutes (5 million years) of video content will cross the Internet each month. According to the Cisco 2020 Forecast, that is one million minutes of video streamed or downloaded every second [1] . The rapidly increasing consumption

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Standardization for digital video compression has shown significant evolution over the last three decades. Starting in 1988 with ITU-T H.261 as the first such standard that was practical for consumer use, ISO/IEC MPEG-1 and H.262/MPEG-2 video (the latter jointly standardized by ITU-T and ISO/IEC) were developed very soon thereafter, creating the first wave of broad usage of digital technology in consumer

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Some methods developed in reversible data hiding (RDH) make use of prediction for data embedding for original pixel estimation. Predicators may also be exploited in RDH in encrypted image (RDHEI); this has become a research interest in recent years because of the development of cloud computing and a need for content owner privacy. This paper presents a high-capacity reversible data hiding in encrypted

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

We propose an interactive contour extraction method inspired by a skill often adopted in sketching: an artist usually sketches an object by first drawing lots of short, directional, and redundant strokes, then following these small strokes to draw the final outline of the object. Our method simulates this process. To extract a contour, our method relies on user interaction, which provides us with a

In this paper, we propose a novel deep learning-based virtual reality image quality assessment method that automatically predicts the visual quality of an omnidirectional image. In order to assess the visual quality in viewing the omnidirectional image, we propose deep networks consisting of virtual reality (VR) quality score predictor and human perception guider. The proposed VR quality score predictor

Most existing blind image quality assessment (BIQA) methods belong to supervised methods, which always need a large number of image samples and expensive subjective scores for training a quality prediction model. In this paper, we focus our attention on the unsupervised BIQA methods and put forward a novel unsupervised approach. The main idea of our method is to quantify the image quality degradation

Tensor completion recovers missing components of multi-way data. The existing methods use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor optimization for data completion. In fact, these two kinds of tensor ranks represent different high-dimensional data structures. In this paper, we propose to exploit the two kinds of data structures simultaneously for image recovery through

Most motion-blurred images captured in the real world have spatially-varying point-spread functions, and some are caused by different positions and depth values, which cannot be handled by most state-of-the-art deblurring methods based on deconvolution. To overcome this problem, we propose a depth-aware motion blur model that treats a blurred image as an integration of a sequence of clear images. To

The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process. Such an approximation has not yet been rigorously examined, and standard dictionaries often lead to non-optimal performance when used for computing thresholded features. In this paper, we first present two theoretical recovery guarantees for

Due to the shortcomings of the weakly supervised and fully supervised object detection (i.e., unsatisfactory performance and expensive annotations, respectively), leveraging partially labeled images in a cost-effective way to train an object detector has attracted much attention. In this paper, we formulate this challenging task as a missing bounding-boxes’ object detection problem. Specifically, we

As we move toward large-scale object detection, it is unrealistic to expect annotated training data, in the form of bounding box annotations around objects, for all object classes at sufficient scale; therefore, the methods capable of unseen object detection are required. We propose a novel zero-shot method based on training an end-to-end model that fuses semantic attribute prediction with visual features

By providing angular and spatial information of light rays, light field images are widely used in many applications. To capture large field of view light field, existing approaches either stitch small field of view light fields, whose apertures are also small, or leverage specialized equipments, which are not accessible to ordinary users. In this paper, we present a method to extract fully 360° field

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem

Object detection has received significant interest in the research field of computer vision and is widely used in human-centric applications. The occlusion problem is a frequent obstacle that degrades detection quality. In this paper, we propose a novel object detection framework targeting robust object detection in occlusion. The proposed deep learning-based network consists mainly of two parts: 1)

Clothing attribute recognition, especially in unconstrained street images, is a challenging task for multimedia. Existing methods for multi-task clothing attribute prediction often ignore the relation between specific attributes and positions. However, the attribute response is always location-sensitive, i.e., different spatial locations have various contributions to attributes. Inspired by the locality

Recently, sparse representation-based classification (SRC) has been widely studied and has produced state-of-the-art results in various classification tasks. Learning useful and computationally convenient representations from complex redundant and highly variable visual data is crucial for the success of SRC. However, how to find the best feature representation to work with SRC remains an open question

Model drifting is one of the knotty problems that seriously restricts the accuracy of discriminative trackers in visual tracking. Most existing works usually focus on improving the robustness of the target appearance model. However, they are prone to suffer from model drifting due to the inappropriate model updates during the tracking-by-detection. In this paper, we propose a novel update-pacing framework

Person re-identification (re-ID) has been a popular topic in computer vision and pattern recognition communities for a decade. Several important milestones such as metric-based and deeply-learned re-ID in recent years have promoted this topic. However, most existing re-ID works are designed for closed-world scenarios rather than realistic open-world settings, which limits the practical application

Compressive sensing (CS)-based image coding scheme has been enthusiastically studied, but it still has a poor rate-distortion performance compared with the traditional image coding techniques. In this paper, we propose a CS multi-layer residual coding scheme to rectify this problem to a certain extent. By dividing CS measurements into multi-layers and predicting a particular layer’s measurements with

This paper presents methods based on convolutional neural networks (CNNs) for removing compression artifacts. We modify the Inception module for the image restoration problem and use it as a building block for constructing blind and non-blind artifact removal networks. It is known that a CNN trained in a non-blind scenario (known compression quality factor) performs better than the one trained in a

Multiuser video transmission, where the server transmits videos to multiple users that require different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate system resources such as transmission power and channels to multiple users to achieve the best system performance

Image datasets play a pivotal role in advancing computer vision and multimedia research. However, most of the datasets are created by extensive human effort and are extremely expensive to scale-up. To address these issues, several automatic and semi-automatic approaches have been proposed for creating datasets by refining web images. However, these approaches either include significant redundant images

In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be effective for hashing retrieval. However, most of the triplet-based deep networks treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider the order relations of the binary codes and ignore the hash encoding when learning

Existing cross-media retrieval methods are mainly based on the condition where the training set covers all the categories in the testing set, which lack extensibility to retrieve data of new categories. Thus, zero-shot cross-media retrieval has been a promising direction in practical application, aiming to retrieve data of new categories (unseen categories), only with data of limited known categories

Future autonomous spacecraft rendezvous with uncooperative or unprepared objects will be enabled by vision-based navigation, which imposes great computational challenges. Targeting short duration missions in low Earth orbit, this paper develops high-performance avionics supporting custom computer vision algorithms of increased complexity for satellite pose tracking. At algorithmic level, we track 6D

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

As one of the indispensable components in convolutional neural network (CNN), loss function assists in updating parameters of CNN models during the training phase. Generally, different loss functions can assist convolutional neural network (CNN) to learn different feature representations, and different feature representations can be treated as different knowledge learned from objects. In this paper

Data association is one of the key research in tracking-by-detection framework. Due to frequent interactions among targets, there are various relationships among trajectories in crowded scenes which leads to problems in data association, such as association ambiguity, association omission, etc. To handle these problems, we propose hypothesis-testing based tracking (HTBT) framework to build potential

Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design a new interaction mechanism that actively queries seeds for guiding the user to label. Our method enforces sparsity and diversity

To accomplish robust watermark extraction in reversible watermarking both in plaintext domain and encrypted domain, a separable robust reversible watermarking in encrypted 2D vector graphics is proposed in this paper. Firstly, a content owner uses a key to scramble the polar angles of the vertices to encrypt the graphics in the polar coordinate system. Consequently, a watermark embedder maps the encoded

High dynamic range (HDR) imaging techniques have witnessed a great improvement in the past few decades. However, saliency detection task on HDR content is still far from well explored. In this paper, we introduce a multi-exposure decomposition-fusion model for HDR image saliency detection inspired by the brightness adaption mechanism. The proposed model is composed of three modules. Firstly, a decomposition

Zero-shot learning (ZSL) can be formulated as a cross-domain matching problem: after being projected into a joint embedding space, a visual sample will match against all candidate class-level semantic descriptions and be assigned to the nearest class. In this process, the embedding space underpins the success of such matching and is crucial for ZSL. In this paper, we conduct an in-depth study on the

In this paper, we propose an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented

In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels. In real-world applications, these intra-camera labels can be readily captured by tracking algorithms or few manual annotations, when compared with cross-camera labels. In this case, it is very difficult to explore the

Person attribute recognition aims to identify the attribute labels from the pedestrian images. Extracting contextual relation from the images and attributes, including the spatial-semantic relations, the spatial context and the semantic correlation, is beneficial to enhance the discrimination of the features for recognizing the attributes. Thus, this work proposes a sequence contextual relation learning

This study proposes a video retargeting method using deep neural network-based object detection. First, the meaningful regions of the input video denoted by bounding boxes of the object detection are extracted. In this case, the area is defined considering the size and number of bounding boxes for objects detected. The bounding boxes of each frame image are considered as regions of interest (RoIs)