Journal Information
Pattern Recognition Letters
http://www.journals.elsevier.com/pattern-recognition-letters/
Impact Factor:
1.586
Publisher:
ELSEVIER
ISSN:
0167-8655
Viewed:
4717
Tracked:
12

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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 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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