Journal Information
Pattern Recognition Letters
http://www.journals.elsevier.com/pattern-recognition-letters/
Impact Factor:
1.586
Publisher:
ELSEVIER
ISSN:
0167-8655
Viewed:
4969
Tracked:
13

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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 Multimodal Fusion for Pattern Recognition (MFPR)
Submission Date: 2017-04-30

The main purpose of this special issue is to consolidate and to strengthen the relationships between the Multimodal data fusion research with Pattern recognition with a double objective: a) to improve scientific and technological results obtained by Multimodal data fusion research, which is expected to lead to a knowledge breakthrough in the areas of Pattern Recognition b) to allow dealing with the new research challenges which are raising both in Pattern Recognition and Computer Vision. An important additional motivation for this special issue is to promote the incorporation of new methodological proposals that currently show great promise, such as 'Machine Learning', 'Big Data' 'Deep Learning' or 'Digital Image Processing'. We also intend to extend the traditional areas of application of Pattern Recognition to new and interesting research where theoreticians and practitioners from academic fields and industries worldwide are currently interested, such as: Neuroscience, Healthcare, Robotics, Agriculture, Smart Cities, etc. In these fields, new Multimodal Interaction technologies based on Machine Learning, Pattern Recognition and Computer Vision that formed the backbone of next generation technologies, are starring to play a central role for the development of new generation of truly user-friendly systems. Recommended topics include (but are not limited to) the following: - Multimodal data fusion - Mathematical modelling for multimodal data - Multimodal for signal processing - Multimodal micro facial Expression - Multimodal for Computer Imagery - Multimodal social media data - Multimodal retrieval systems - Multimodal Big data Analytics - Novel dataset and benchmark for Multimodal data - Data mining and knowledge discovery and data visualization - Deep learning, supervised learning and un-supervised learning
Last updated by Dou Sun in 2017-04-03
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
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
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