Journal Information
Pattern Recognition Letters
http://www.journals.elsevier.com/pattern-recognition-letters/
Impact Factor:
1.586
Publisher:
ELSEVIER
ISSN:
0167-8655
Viewed:
4311
Tracked:
10

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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 2016-11-23
Special Issues
Special Issue on Machine Learning and Applications in Artificial Intelligence
Submission Date: 2017-01-31

Machine learning (ML) deals with designing and developing algorithms to evolve behaviors based on empirical data. ML has the ability to adapt to new circumstances and to detect and extrapolate patterns. One key goal of machine learning is to be able to generalize from limited sets of data. Many successful applications of machine learning exist already, including algorithms to identify spam or to stop credit card fraud, systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, extract knowledge from bioinformatics data, images and video, identify hear failures, and a long list of interesting and extremely useful applications. The main scope of this special issue is to bring together applications of machine learning in artificial intelligence (human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and social sciences, bioinformatics, robotics, etc.) in order to give a wide landscape of techniques that can be successfully applied and also to show how such techniques should be adapted to each particular domain. Topics of interests include, but are not limited to Classification, regression and prediction; Clustering; Kernel methods; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Information retrieval; Natural language processing; Design and diagnosis; Deep learning; Probabilistic Models and Methods; Vision and speech perception; Robotics and control; Multi-agent systems; Game playing; Bioinformatics; Social sciences; Streaming data; Music Modelling and Analysis; Industrial, financial and scientific applications of all kind.
Last updated by Dou Sun in 2016-12-13
Special Issue on Data Representation and Representation Learning for Video Analysis
Submission Date: 2017-02-28

This call is oriented to bring together researchers from different areas related to designing and learning improved data representations for video understanding applications. This has received increasing interest in the research community due to the need for interpreting the large amount of video data generated every day. We are soliciting papers focusing on techniques such as deep learning and the design of representations not only based on low-level feature descriptors, but also on the use of clues inferred from the scene, such as the presence of objects and scene information. The main topics for this special issue include, but are not limited to, the following: - Spatial-temporal feature descriptors; - Data representations based on deep learning techniques; - Image processing techniques to recover improved data representation; - Extraction of semantic representations for video data representation, such as object importance, scene context; - Applications such as activity recognition, semantic video summarization, video captioning, action retrieval and anomaly detection; - Data representation for egocentric video analysis; - Description of video scenes; - Efficient data representation and representation learning for large amounts of data.
Last updated by Dou Sun in 2016-11-23
Special Issue on Deep Learning for Pattern Recognition
Submission Date: 2017-02-28

Pattern Recognition is one of the most important branches of Artificial Intelligence, which focuses on the description, measurement and classification of patterns involved in various data. In the past 60 years, great progress has been achieved in both the theories and applications of pattern recognition. A typical pattern recognition system is composed of preprocessing, feature extraction, classifier design and postprocessing. Nowadays, we have entered a new era of big data, which offers both opportunities and challenges to the field of Pattern Recognition. We should seek new Pattern Recognition theories to be adaptive to big data. We should push forward new Pattern Recognition applications benefited from big data. Deep Learning, which can be treated as the most significant breakthrough in the past 10 years in the field of pattern recognition and machine learning, has greatly affected the methodology of related fields like computer vision and achieved terrific progress in both academy and industry. It can be seen as a resolution to change the whole pattern recognition system. It achieved an endtoend pattern recognition, merging previous steps of preprocessing, feature extraction, classifier design and postprocessing. It is expected that the development of deep learning theories and applications would further influence the field of pattern recognition. This special issue mainly focuses on Deep Learning for Pattern Recognition (DLPR). We are soliciting original contributions, of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in deep learning for pattern recognition. Original papers to survey the recent progress in this exciting area and highlight potential solutions to common challenging problems are also welcome. The topics include, but not limited to: - Deep learning architecture for pattern recognition - Optimization for deep learning - Supervised deep learning - Unsupervised deep learning - Sparse coding in deep learning - Transfer learning for deep learning - Deep learning for feature representation - Deep learning for face analysis - Deep learning for object recognition - Deep learning for scene understanding - Deep learning for text recognition - Deep learning for dimension reduction - Deep learning for activity recognition - Deep learning for biometrics - Performance evaluation of deep learning
Last updated by Dou Sun in 2016-11-23
Special Issue on Video Surveillance-oriented Biometrics
Submission Date: 2017-03-30

Video surveillance-oriented biometrics is a very challenging task and has tremendous significance to the security of public places. With the growing threat of crime and terrorism to public security, it is becoming more and more critical to develop and deploy reliable biometric techniques for video surveillance applications. Traditionally, it has been regarded as a very difficult problem. The low-quality of video frames and the rich intra-personal appearance variations impose significant challenge to previous biometric techniques, making them impractical to real-world video surveillance applications. Fortunately, recent advances in computer vision and machine learning algorithms as well as imaging hardware provide new inspirations and possibilities. In particular, the development of deep learning and the availability of big data open up great potential. Therefore, it is the time that this problem be re-evaluated. This special issue will provide a platform for researchers to exchange their innovative ideas and attractive improvements on video surveillance-oriented biometrics. The following list suggests topics of interest (but not limited to): - Real-time processing and recognition of faces from long surveillance videos; - Solutions to complex illumination, large pose, occlusion and image blur in surveillance videos; - Solutions to large distance and low resolution face and gait recognition; - Summarization of surveillance videos; - Novel machine learning algorithms for biometrics under surveillance conditions; - Video frame quality evaluation approaches; - Image set modeling for video data analysis; - Face detection, tracking, and alignment in surveillance video frames; - Computation of soft-biometrics and attributes from surveillance videos; - Announcement of new video surveillance databases for biometrics; - Heterogeneous face recognition using multi-modal surveillance data; - Efficient algorithm for massive video data analysis; - Survey papers regarding the status and trends for surveillance video-based biometrics.
Last updated by Dou Sun in 2016-11-23
Special Issue on Pattern Discovery from Multi-Source Data
Submission Date: 2017-05-31

Advanced data acquisition technologies have been producing massive amounts of data in engineering sciences, and computer science. In addition to volume, data are naturally comprised of multiple representations in many real applications since only single-source data do not always meet all types of scenarios. For example, in image analysis, images are represented by local features and global features. Usually, different sources describe different characteristics of images. Thanks to the massive volume and multi-source structure of data, studies have shown that, it is very difficult to deal with multi-source data using conventional analysis tools. We have also noticed that pattern recognition from multi-source data is different activity than that from single-source data. Thus the understanding and analysis of multi-source data has been a very popular topic in machine learning and computer vision. Meanwhile the advent of multi-source data creates new challenges for current information technology. In this special issue, we invite papers that address many of the challenges of pattern discovery from multi-source data.
Last updated by Dou Sun in 2016-12-16
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
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