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The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval

With the current global outbreak of COVID-19, an increasing number of people are suffering from negative mental states and mental disorders. We propose a multimodal psychological computational technology in a universal environment. We establish a mental health database following a naturalistic paradigm as well as a long-term ubiquitous interpretable psychological computing model based on prior knowledge

In this article, we develop an end-to-end clothing collocation learning framework based on a bidirectional long short-term memories (Bi-LTSM) model, and propose new feature extraction and fusion modules. The feature extraction module uses Inception V3 to extract low-level feature information and the segmentation branches of Mask Region Convolutional Neural Network (RCNN) to extract high-level semantic

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With the rapid growth of Android applications, there is an urgent need for powerful Android malware detection technology nowadays. Existing classification models can be summarized with the following two steps-feature extraction and classification model learning. To further enhance the representation ability of existing classification models, this article presents an Android malicious application detection

In multilabel text classification, the label information of training instances may be incomplete due to various factors, such as expensively manual labeling, errors, etc. Hence, imposing big challenges to training classifiers. To tackle the problem of the incomplete labels, we proposed to exploit label correlations, and further jointly leverage manifold regularization and label confidence constraint

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The papers in this special section focus on the topic of urban multimedia computing. Presents emerging methods in multimedia computing for urban analysis and applications. Urban computing is a process of acquisition, integration, analysis, and understanding of the urban data generated from various sources, e.g., sensors, devices, and social media, for various applications. Multimedia computing plays

Self-driving cars leverage on semantic segmentation to understand an urban scene. However, it is costly to collect segmentation labels, thus, synthetic datasets are used to train segmentation models. Unfortunately, the synthetic to real domain shift causes these models to perform poorly. Prior works use adversarial training to align features of both synthetic and real-world images. We observe that

8 k ultra-high definition (UHD) is paving the way for the next-generation video systems. In the audiovisual industry, besides delivering a more immersive experience, it is a mean to smooth spatial artifacts during video sampling. In the medical industry, it may provide surgeons with increased reality in surgeries such as endoscopy. Nevertheless, researchers are struggling to meet the high throughput

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Human action recognition is the basis technology of human behavior understanding, and it is a research hotspot in the field of computer vision. Recently, some studies show skeleton data (i.e., joint points and edges) is naturally more conducive to mining the connotation of human action, so exploring the relationship between joints and bones is helpful to improve action recognition. In this article

Multiview clustering has attracted much attention due to its ability to aggregate various source information and many advanced approaches have been proposed in the literature. However, there are still two major issues that need to be further explored: i) how to efficiently handle large-scale data; ii) how to effectively incorporate the complementary multiple sources. In this article, we fulfill a unified

Image retrieval on the social media platforms propels the tourism information sharing on the Internet. Existing image retrieval methods lack the capability of discovering global statistical distribution of feature representations at multiple scales. In this article, we propose a gated multiscale NetVLAD network, which constructs feature pyramid network based on ResNet backbone and computes NetVLAD

Cross-modal retrieval has become a hot issue in past years. Many existing works pay attentions on correlation learning to generate a common subspace for cross-modal correlation measurement, and others use adversarial learning technique to abate the heterogeneity of multimodal data. However, very few works combine correlation learning and adversarial learning to bridge the intermodal semantic gap and

With the increasing popularity of online shopping, searching for products with images for item retrieval has gradually become an effective approach. This trend is especially evident in the fashion industry. In common media, clothing items are usually worn on the human body. They can be straightforwardly segmented from the source media by utilizing detection or parsing algorithms. However, this may

Recently, during natural disasters, the use of social media as a source of actionable information has increased significantly. Finding disaster-related tweets and analyzing their textual content and images can help government agencies and rescue organizations make better decisions. The main challenge of disaster-related message detection on social networking sites is how to identify posts related to

FU-PCM is an effective clustering mode by inducing the regularization constraint in C-means to avoid the interference of noises and outliers. However, it is difficult to obtain the satisfactory performance in the context of imbalance category distribution. To address above problem, first, we propose a novel clustering model, which exploits Pearson Correlation Coefficient to auto-balance the optimal

To evaluate the driving behavior of unmanned vehicles, the testing of driving algorithms using urban road images is necessary. In this article, we propose a framework using generative adversarial networks (GANs) with structural information for image style transfer: StructureGAN and GradientGAN. Different types of urban image transfers are generated using the proposed framework, such as day to night

In this article, an effective approach is proposed for nonrigid structure from motion to deal with the image sequence with a complex deformation. In the proposed method, a three-dimensional (3-D) structure is first divided into multiple local trajectory groups by utilizing the distance weights of the pairwise points. For each local trajectory group, a weak estimator is constructed by means of the kernel

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With the explosion of multimedia data, machine learning applications are proliferating. Privacy concerns have recently resurfaced, due to a few prominent incidents of user data leakage, which divulged sensitive information to other people. Multimedia data platform providers––social networks, their affiliates or governments with access to users’ content––use algorithms to profile users by extracting

Multimedia research has long moved beyond laboratory experiments and is being rapidly deployed in real-life applications including advertisements, search, security, automated driving, and healthcare. Hence, the developed algorithms now have a direct impact on the individuals using the abovementioned services and the society as a whole. While there is a huge potential to benefit the society using such

High expansion of urban population and infrastructure complemented by recent geopolitical world events, triggered an increasingly alarming number of threats. Law enforcement authorities are now challenged to redesign societal security concepts. Although current technological advances eased the information access, e.g., via video surveillance cameras, satellite data, drones, wearables, manual analysis

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Vehicle reidentification (reID) targets at matching same vehicle images captured from multicameras, which has become a hot topic in recent years. However, it poses the critical but challenging problem that is caused by complicated environments and diversified illuminations. Inspired by the benefit of attributes that contain detailed descriptions, this article proposes a novel attribute-guided network

The block-based representation learning method has been proven to be a very effective method for person reidentification (Re-ID), but the features extracted by the existing block-based approach tend to have a high correlation among different blocks. Also, these methods perform less well for persons with large posture changes. Thus, part-based nondirect coupling representation learning method is proposed

With the maturity of artificial intelligence technology, large-scale visual geolocalization (LSVGL) is increasingly important in urban computing, where the task is to accurately and efficiently recognize the geolocation of a given query image. The main challenge of LSVGL faced by many experiments due to the appearance of real-word places may differ in various ways while perspective deviation almost

The smart healthcare system is an electronic patient records (EPR) sharing system, which significantly helps sharing of EPR data and provides appropriate medical assistance for the patients and a more suitable platform for the potential researchers. However, the security of EPR data is still a major issue in such systems. In this paper, we develop a compression-then-encryption-based dual watermarking

Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and transmitted to the back-end for further processing. In this article, we present a lossless key-point sequence compression approach for efficient feature coding. The essence of this predict-and-encode strategy is to eliminate

Maintaining electronic patient records is the primary role for the fruitful implementation of the telehealth services in urban areas to enhance the quality of healthcare. These records frequently shared in-between professionals and healthcare centers so as to develop efficient tool(s) for accurate diagnosis purpose. However, protection of these records is a challenging task in the current era. In this

With the rapid advancement of real-time rendering quality in digital art, the use of computer vision techniques has become popular in producing aesthetically pleasing stylized images of natural scenes. However, the processing time to produce a stylized image is high, especially in resource-constrained environments, such as smart devices. Most stylized imaging approaches are unable to preserve the fine

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