Journal Information
Pattern Recognition Letters
http://www.journals.elsevier.com/pattern-recognition-letters/
Impact Factor:
1.995
Publisher:
ELSEVIER
ISSN:
0167-8655
Viewed:
6734
Tracked:
23

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Call For Papers
Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include:

• Statistical, structural, syntactic pattern recognition;
• Neural networks, machine learning, data mining;
• Discrete geometry, algebraic, graph-based techniques for pattern recognition;
• Signal analysis, image coding and processing, shape and texture analysis;
• Computer vision, robotics, remote sensing;
• Document processing, text and graphics recognition, digital libraries;
• Speech recognition, music analysis, multimedia systems;
• Natural language analysis, information retrieval;
• Biometrics, biomedical pattern analysis and information systems;
• Scientific, engineering, social and economical applications of pattern recognition;
• Special hardware architectures, software packages for pattern recognition.

We invite contributions as research reports or commentaries.

Research reports should be concise summaries of methodological inventions and findings, with strong potential of wide applications.
Alternatively, they can describe significant and novel applications of an established technique that are of high reference value to the same application area and other similar areas.

Commentaries can be lecture notes, subject reviews, reports on a conference, or debates on critical issues that are of wide interests.

To serve the interests of a diverse readership, the introduction should provide a concise summary of the background of the work in an accepted terminology in pattern recognition, state the unique contributions, and discuss broader impacts of the work outside the immediate subject area. All contributions are reviewed on the basis of scientific merits and breadth of potential interests.
Last updated by Dou Sun in 2017-08-07
Special Issues
Special Issue on Learning Compact Representations for Scalable Visual Recognition and Retrieval
Submission Date: 2018-01-31

Motivations and Topics: With the explosive growth of visual data, traditional hand-crafted features or learning-based representations will induce inapplicable computational complexity and large memory costs, due to exhausting computations in large-scale and high-dimensional feature space. Therefore, these conventional methods are lack of scalability for large-scale visual applications, e.g. image/video recognition and retrieval. It is highly desirable to learn much more compact feature representations to reduce computational loads for massive visual data and make big data analysis more feasible. In recent years, compact feature learning algorithms have been intensively exploited and attracted broad interests in visual analysis. For instance, benefiting from the hashing technique, we can obtain compact binary representations, based on which efficient XOR computations in the Hamming space can operate in constant time. The above compact feature learning approaches have been proved to achieve promising performance in various large-scale visual applications, such as content-based image/video retrieval and recognition. In addition, these techniques will be essential for the applications on portable/wearable devices. The special issue will focus on the most recent progress on compact representation learning for a variety of large-scale visual data analysis, such as content-based image/video retrieval, image/video recognition, annotation and segmentation, object detection and recognition, visual processing and affective computing. This special issue will also target on related feature selection, subspace learning and deep learning techniques, which can well handle large-scale visual tasks. The primary objective of this special issue fosters focused attention on the latest research progress in this interesting area. The special issue seeks for original contributions of work, which addresses the challenges from the compact representation learning and the related efficient representation learning algorithms for large-scale visual data. The list of possible topics includes, but not limited to: Novel compact representation learning algorithms Large-scale visual (image, video) indexing algorithms Learning-based or data-dependent binary coding/hashing methods Novel vector quantization algorithms Visual recognition (e.g., detection, categorization, segmentation) with discriminative representation learning techniques Compact feature learning for object classification/detection/retrieval/tracking Novel applications of compact representation learning Deep learning techniques for compact representation learning Efficient feature extraction methods for visual data analysis Efficient learning algorithms for visual data representation Submission Guideline Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Pattern Recognition Letters journal at https://www.elsevier.com/journals/pattern-recognition-letters/0167-8655/guide-for-authors/. All the papers will be peer-reviewed following the Pattern Recognition Letters reviewing procedures. When submitting their papers through the online system, Authors should select the acronym “SI:LCR4SVRR” to make it clear that they are submitting to this SI.
Last updated by Dou Sun in 2017-01-15
Special Issue on Multiple-task Learning for Big Data (ML4BD)
Submission Date: 2018-03-31

Big Data is a term that describes the large volume of data-both structured and unstructured. With the rapid development of new information technologies such as smart phone, mobile game platforms, smart home devices, smart health devices, and wearable computation devices, the amount of created and stored data on global level is almost inconceivable and it just keeps growing. These data is s- large and complex that traditional data processing applications are incapable of dealing with them. There are many challenges when addressing big data problem, such as data acquisition, data curation, data storage, data search, data transfer and sharing, data visualization, data query and retrieval, information security, and data analysis (e.g., prediction, user behavior analysis). Big data requires novel data processing techniques t- solve some of these challenges jointly, which is related t- Multiple-task Learning methodologies. Multiple-task Learning is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for each task-specific model, when compared t- training each of the models separately. Multiple-task Learning is an approach of inductive transfer that improves generalization by using domain information contained in the training signals of related tasks as inductive bias. It does this by learning multiple tasks in parallel using a shared representation, based on the assumption that all tasks can help each other in learning. MTL works because regularization induced by requiring an algorithm t- perform well on a related task can be superior t- regularization that prevents overfitting by penalizing all complexity uniformly. MTL may be particularly helpful if all related tasks share significant commonalities and are slightly under sampled. With big structured and unstructured data, different tasks on the same data or related data are suitable for MTL framework. Therefore, Multiple-task Learning for Big Data (MTL4BD) have broad applications in many fields, such as online recommendation system, smart home, smart health care, robotics, medical imaging, multimedia application, computer vision, human computer interaction and language processing, etc. We plan t- receive about 45 paper submissions from around 30 Universities and research institutions and we will accept about 15 papers plus a review paper totally.
Last updated by Dou Sun in 2017-04-13
Special Issue on Learning and Recognition for Assistive Computer Vision
Submission Date: 2018-06-30

Assistive Computer Vision refers to systems that support people with physical and mental problems to better perform daily tasks enhancing their quality of life. The advances in learning and recognizing patterns are allowing a point of view in the definition and development of more efficient and effective assistive frameworks. In the light of this, it is important to collect the most recent advancements in learning and recognition algorithms to be exploited in different applications to be employed to assist the modern society. The aim of the special issue is to gather papers in which machine learning and pattern recognition are the key core in the design of advanced assistive computer vision systems to help human in tasks such as: - Rehabilitation - Training - Mobility - Assessment and diagnosis of physical and cognitive diseases - Improving quality of Life - Remote Healthcare - Safe and security - Remote Surgery - Ambient Assisted Living - Augmented Perception, Attention and Memory We will invite authors to contribute with high quality paper that will stimulate the research community on building theory and applications of machine learning and pattern recognition to be used in real-life environments for assistive computer vision technologies.
Last updated by Dou Sun in 2017-04-13
Special Issue on Pattern Recognition Techniques for Non Verbal Human Behavior (NVHB)
Submission Date: 2018-07-31

Fundamental Cues for Non-Verbal behavioral are human communication and interaction. Despite Significant advances in recent years, state of the art human-machine systems still falls short in sensing, analyzing and fully understanding cues naturally expressed in everyday settings. Two of the most important non-verbal cues, evidenced by a large body of work in experimental psychology and behavioral sciences, are visual behavior and body language. Widely anticipated in HCI is that computing will move to the background, weaving itself into the fabric of our everyday living and projecting the human user into the foreground. To realize this goal, next-generation computing will need to develop human-centered user interfaces that respond readily to naturally occurring, multimodal, human communication. These interfaces will need the capacity to perceive, understand, and respond appropriately to human intentions and cognitive- emotional states as communicated by social and affective signals. Motivated by this visionof the future, automated analysis of nonverbal behavior has attracted increasing attention in diverse disciplines, including psychology, computer science, linguistics, and neuroscience. Promising approaches have been reported, especially in the areas of facial expression and multimodal communication. Yet, increasing evidence suggests that deliberate or posed behavior differs in appearance and timing from that which occurs in daily life. Approaches to automatic behavior analysis that have been trained on deliberate and typically exaggerated behaviors may fail to generalize to the complexity of expressive behavior found in real-world settings. This Virtual Special Issue (VSI) intends to bring together researchers and developers from academic fields and industries worldwide working in the broad areas of computer vision and promote community-wide discussion of ideas that will influence and foster continued research in this field for the betterment of human mankind. Papers submitted to this VSI and accepted for publication will be spread through several regular issues, since each accepted paper will be published as soon as possible without waiting until all submissions to the VSI are in final status. The accepted papers will also be gathered as part of a VSI that will be available exclusively online and will be gradually built up as the individual articles are published online. Recommended topics are given below: - Intelligent visual surveillance - Deep learning based Gait recognition - Machine Learning approaches - Semi supervised learning based behavior analysis - Deep learning for facial expression behavior - Real world application of behavior analysis - Time-critical techniques to understand gestural behavior - Sensor data interpretation for live behavior analysis
Last updated by Dou Sun in 2017-08-07
Special Issue on DLVA: Advances in Deep Learning and Visual Analytics for Intelligent Surveillance Systems
Submission Date: 2018-09-30

The increasing sophistication and diversity of threats to public security have been calling critical demand of developing and deploying reliable, secure, and timely efficient visual intelligent surveillance systems in smart cities. For example, visual surveillance for indoor environments, like metro stations, plays an important role both in the assurance of safety conditions for the public and in the management of the transport network. When designing the next generation security solutions, it is crucial to combine sensing, computing, understanding, communication and prediction in such networked-camera systems. Examples include automated video surveillance platforms and smart camera networked systems that are monitoring the behavior, activities, or other changing information for the purpose of influencing, managing, directing, or protecting people. They exhibit a high-level of awareness beyond primitive actions, in support of persistent and long-term autonomy. However, some core problems such as object identification and tracking, and behavior analysis in intelligent surveillance are still affected by a number of practical problems. They typically involve a variety of representation, reasoning and efficiency mechanisms in the context of an extended distance and period of time and low resolution/frame rate in poor quality capturing conditions. Recent progress in computer vision techniques and related visual analytics offers new prospects for an intelligent surveillance system. A major recent development is the massive success resulting from using the deep learning techniques to enable the significant boosting of visual analysis performance and initiate new research directions to understand visual content. For example, convolutional neural networks have demonstrated superiority on modeling high-level visual concepts, while recurrent neural networks have shown promise in modeling temporal dynamics in videos. It has been and will be seen as resolution to change the whole visual recognition systems. It is expected that the development of deep learning and its related visual analytic methodologies would further influence the field of intelligent surveillance systems. This special issue will serve a platform to publish state-of-the-art advancements in this domain of research and seeks for original contributions of work, which addresses the challenges from using deep learning and related techniques to understand and promote the ubiquitous intelligent surveillance systems. Original papers to survey the recent progress in this exciting area and highlight potential solutions to common challenging problems are also welcome. The list of possible topics includes, but not limited to: - Emotion/Gait/Activity/Gesture recognition and prediction - Large-scale video indexing - Pedestrian detection in the wild - Scene understanding and human behavior analysis - Person re-identification and biometric recognition - Summarization of long surveillance videos - Visual analytics for forensics and security applications - Pedestrian and vehicle navigation tracking - Face recognition and verification - Event (abnormal) detection and recognition - Cloud and distributed for visual surveillance - Object tracking and segmentation - Human computer/robot interactions - Data collections, benchmarking and performance evaluations
Last updated by Dou Sun in 2017-10-13
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