In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist sampling theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of CS. This article gives a brief background on the origins of this idea, reviews the basic mathematical foundation of the theory and then goes on to highlight different areas of its application with a major emphasis on communications and network domain. Finally, the survey concludes by identifying new areas of research where CS could be beneficial.

http://romisatriawahono.net/lecture/rm/survey/network%20security/Qaisar%20-%20Compressive%20Sensing%20-%202013.pdf

Compressive sensing: From theory to applications, a survey

Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

/pdf/what-else-does-your-biometric-data-reveal-a-survey-on-soft-1d8sy6v8ai.pdf

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.

Survey on computational-intelligence-based UAV path planning

This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e., distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases.

Audio-Visual Emotion Recognition in Video Clips

Facial expression recognition is one of the hot spots in recent years, it applies in the emotional analysis, pattern recognition and interpersonal interaction. This paper introduces the recent advances and applications in facial expression recognition from the face detection, feature extraction, classification, and the ethnic expression recognition. The methods of feature extraction are divided to several different characteristic categories. Researches of classifications are based on space or time and space. What’s more, according to the facial expression recognition history and achievements, the development of ethnic facial expression recognition and the trend of facial expression recognition are given.

Survey of the Facial Expression Recognition Research

Recently, considerable attention has been focused on the investigation of the use of wireless sensor networks (WSN) to drive autonomous mobile robot for navigation in flexible, indoor environments instead of fixed locations, for their potential as a cheap, easily deployed, and distributed monitoring tool. However, such composite net systems are nontrivial to design for the robot systems typically assume the map known in advance, or they need to build the map while moving around, with the help of acquired sensor network infrastructure information. The key component for determining the robot's pose by the information of physical location of the sensor nodes after they have been deployed is known as the problem of localization. In this paper we propose an environment map free navigation algorithm based on RF wireless sensor networks for an indoor autonomous mobile robot. In our navigation system, the robot can navigate autonomously without the need for a map, by acquiring the information of pre-set radio emission sensors deployed in an indoor environment. By measuring the distance from one sensor node after another using triangular localization method, the robot locates itself and knows its pose. The proposed algorithm is conceptually simple and easy to implement. Our approach is applied to a mobile robot platform, simulation and experimental results show that good localization can be achieved using the proposed method.

An indoor navigation system for autonomous mobile robot using wireless sensor network

Path planning has always been a crucial issue for UAV. The UAVs path planning in multiple missions involves the solution of an optimization problem. Genetic algorithms (GAs) are well applied to solve such problems as a stochastic search method. In this paper, a new method based on genetic algorithm is presented to generate path for UAV in the existence of unknown obstacle environments. The path planning model is based on 2D digital map, and an adaptive evolutionary planner is adopted based on a set of criteria to generate path online to avoid being detected by ground surveillance radar sites. Simulation studies are carried out to verify the effectiveness of the proposed algorithm. We believe the GA algorithm may be of help in the future reseach direction of UAV path planning problem.

Path planning for unmanned aerial vehicle based on genetic algorithm

Emotion recognition has aroused great concern recently. Physiological signals show its objective in the field of emotion recognition. This paper introduces emotion recognition system and physiological signals processing. The recognition system can be divided into four sections: signal preprocessing, biological feature extraction, feature matching to and feature classification. For each part, we studied existed methods and discussed their performance and characteristics. Lastly, the trend of emotion recognition for physiological signals was given.

Emotion Recognition Based on Physiological Signals

Recently, considerable attention has been focused on the investigation of the use of wireless sensor networks (WSN) to drive mobile robot for efficient exploration in unknown environments. The process of navigation depends heavily on estimating the position of the features provided by sensor within the entire environment. This paper proposes a novel navigation algorithm based on RF wireless sensor networks to simultaneous localization and mapping (SLAM) for an indoor autonomous mobile robot. Triangulation localization method is employed to make the robot locates itself and knows its pose. A simple yet efficient two-step particle filter is applied for map building. Simulation and experimental results show that good localization can be achieved using the proposed method.

Guosheng Yang

Papers

Survey of the Facial Expression Recognition Research

An indoor navigation system for autonomous mobile robot using wireless sensor network

Path planning for unmanned aerial vehicle based on genetic algorithm

Emotion Recognition Based on Physiological Signals

Simultaneous localization and mapping (SLAM) for indoor autonomous mobile robot navigation in wireless sensor networks