Journal Information
Pattern Recognition Letters (PRL)
Impact Factor:

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 2018-12-09
Special Issues
Special Issue on  Artificial Intelligence for Distributed Smart Sensing
Submission Date: 2019-12-31

The goal of Artificial Intelligence (AI) is to reproduce biological intelligence in the form of adaptive machines. The path towards this goal is characterized by several steps, among which the integration of the AI with Smart Sensors (SS) is fundamental. SS and, more generally, Smart Cyber Physical Systems are nowadays significantly impacting the everyday life of citizens and, in perspective, they will become pervasive in every aspect of human life from public health and well-being to home, infrastructures and environment management. It is only thanks to the integration of AI and SS that computers can increasingly see, hear, touch, smell and taste and so become aware and capable to positively interact with the environment in which they are deployed. The research activity (industrial and scientific) in AI is still very fragmented. In fact the development of an intelligent system capable of dealing with all the senses and adapting to different contexts is still relatively far. Furthermore, the results obtained on different sensing areas are still very unbalanced. Indeed, the obtained results are impressive for some senses and weak for the others. Into the first category it is possible to include sight (with vision systems made by large companies and research institutes), hearing (with the speech to text systems of many devices for everyday use) and the more general "comprehension". For the other senses, much more work remains to be done: touch sensors are little more than devices able to understand if "I’m touching something", whereas on smell and taste there is still much to be done. Another important issue is related to the possibility of exploiting collaborative approaches through Distributed Architectures. In this kind of applications, SS are spread into the environment of interest where some kind of “social intelligence” is generated. Many applications of such an architecture are possible in smart cities, smart industries, smart buildings, etc. The improvements will necessarily have to take place at different levels: physical (sensors with increased discriminatory capabilities, robustness and stability), data processing (sensors equipped with electronics for signal conditioning in order to make them "informative"), data communication (sensors equipped with different solutions for sending/receiving data following for example the IoT paradigm) and, finally, understanding the data (with AI). The aim of this Special Issue is to bring together academics and industrial practitioners to exchange and discuss the latest innovations and applications of AI in the domain of SS and DSS. TOPICS - Wired and wireless solutions - New sensor technologies - Internet of Things - Computer Vision - Natural Language processing - Deep and Reinforcement Learning - Ontology solutions - Sensor Network - Critical applications - Soft Computing - Computational Intelligence - Neurocomputing/Neural Systems - Case studies - Multimedia Learning - Classification and clustering algorithms for DSS. - Solutions for Industry 4.0 (energy, logistics, optimization, ...) - Agent-based solutions - Wearable solutions - Big data analysis - Ambient assisted living - Hazard detection - Real Time - Security - Applications of AI - Multi-modal distributed sensors - Distributed sensing applications all referred to SS and/or to DSS.
Last updated by Dou Sun in 2019-02-17
Special Issue on Deep Multi-source Data Analysis (DMDA)
Submission Date: 2020-01-31

Internet makes data acquisition easy and cheap, leading to multi-source data pervasive in the real life. Multi-source data provides enough information that often makes the models can be learned effectively. However, multi-source data is also complex, heterogeneous, and very large in size where inappropriate handling of it will produce ineffective learning models. This inevitably causes the multi-source data analysis a challenging task in many applications. The conversional shallow analysis techniques have been shown to be difficult in dealing with big multi-source data due to their massive volume and multi-source structure, while the most popular deep analysis techniques encounter a lot of limitations (e.g., huge computation power and huge numbers of tuning parameters) in order to make it proficient in the specific domains. Therefore, the study of Deep Multi-source Data Analysis (DMDA) (including novel shallow learning techniques, advanced deep learning techniques, and especially their hybrid) has been a very popular topic in the domain of machine learning and computer vision. In this special issue, we invite papers to address many challenges of big multi-source data analysis. Specifically, to provide readers of this special issue with state-of-the-art background on the topic,we will invite one survey paper, which will undergo the peer review process. The list of possible topics include, but not limited to: Multi-source transaction data analysis Data preprocess of multi-source databases (missing value imputation and feature selection, clustering, and synthesizing/fusion) via shallow learning techniques and deep learning techniques Distributed/paralleled techniques and sampling techniques for big multi-source databases mining Transfer learning among multi-source database Multi-source multimedia data analysis Representation learning (e.g., deep learning methods, local feature extraction methods, and global feature extraction methods) Multi-source data analysis tools and applications (e.g. search, storing, ranking, hashing, and retrieval) Structured/semi-structured multi-source data analysis (e.g., zero-shot learning, one-shot learning, supervised learning, unsupervised learning and semi-supervised learning) Cross-model data analysis (e.g. search and retrieval) via transfer learning and deep learning Multi-task data analysis Similarities/dissimilarities learning from multiple tasks Regularization strategies in multi-task learning or domain adaptation and transfer learning Multi-task learning or domain adaptation or transfer learning for big computer vision and multimedia analysis Large tasks (modals), small sample size learning for multi-task learning, domain adaptation, and transfer learning
Last updated by Dou Sun in 2018-12-09
Special Issue on Advances in Graph-based Representations for Pattern Recognition (AGbR4PR)
Submission Date: 2020-02-28

Graph-based representation and learning/inference algorithms are widely applied to structural pattern recognition, image analysis, machine learning and computer vision. Facing the multitude of scientific problems and the wide applications of graph-based representations, the IAPR TC-15 (Graph-based Representations in Pattern Recognition) promotes a series of workshops called IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition (GbR) since more than 20 years. This series of workshops has benefitted the community in triggering scientific research and exchanging progresses all along years. The 12th edition of GbR was held in Tours, France, in June 2019, and saw several original contributions linked to the actual strong interest for deep learning and artificial intelligence. This special issue should aim to report the last advances in theory, methods and applications using graphs for pattern representation and recognition. The scope ranges from various computing issues like combining machine learning with graphs, graph mining, graph representations of shapes, images and networks, to applications in pattern recognition, computer vision and data mining. The topics of the Special Issue include, but are not limited to: Graph matching Graph-based image segmentation Machine Learning / Deep Learning on graphs Graph representation of shapes Graph-based learning and clustering Data mining with graphs Graph distance and similarity measures Kernel methods for graphs Graph embedding Belief-propagation methods Graph-cuts methods Graphs in computational topology and bioinformatics Graphs in social network analysis
Last updated by Dou Sun in 2019-10-14
Special Issue on Digital Anastylosis of Frescoes challeNgE (DAFNE)
Submission Date: 2020-03-31

To highlight the importance of cultural heritage assets conservation, and promoting restoration of artworks that would otherwise be lost forever, we propose an international challenge to look for solutions that support image reconstruction after destructive phenomena, such as earthquakes or wars. In particular, we focus on reconstruction of frescoes and deal with anastylosis, which is an archaeological term for a reconstruction technique where ruined buildings/monuments are restored using the original architectural elements to the greatest degree possible. The current state-of-the-art research in virtual anastylosis presents several trials and case studies based on a combination of different digital measurements and modelling techniques, accompanied by the interpretation of data coming from documentary sources. Goal of this Special Issue is to collect the best solutions to virtually recomposing destroyed frescoes, starting from the digitalization of their broken collected elements. In this framework, the restoration could be interpreted as a very challenging 'puzzle' formed by original fragments of the destroyed fresco. Critical issues are due to: i) the number of randomly mixed fragments is usually huge; ii) fragments are mostly corrupted, and with general irregular shapes; iii) mismatch of the boundaries of the collected eroded pieces; iv) some pieces have gone irretrievably lost; v) due to extreme fragmentation, presence of spurious/distractors elements, due to pieces of different frescoes also involved in the building collapse. Contributors to this Special Issue will be entitled, at their discretion, to use a number of different cases of destroyed well-known frescoes that have been simulated in order to populate a dataset containing fragmented pieces, useful for the development and testing phases of the challenge. Five different parameters are used for the generation of elements: the number of fragments, their average size, the percentage of missing parts, the percentage of spurious fragments, and the average ratio between the fragment area after the erosion and the original area in the plane tessellation. This initiative can bring to different strategies. The Pattern Recognition community is involved since 1968 and will keenly participate applying advanced computer techniques such as Machine Learning and Deep Learning. But also some interactive solutions can be conceived, in particular involving autistic subjects, favoring their social inclusion in productive activities, exploiting their peculiarities and abilities, and promoting and appreciating their potential. Prospective authors can submit their results related to a solution that reassembles a specific set of elements, discarding the spurious ones. The best papers describing methods and the results will be published, after a peer review, in this Special Issue. Topics of interest include but are not limited to: - Consistent image completion - General wall painting reconstruction algorithms - Image reassembling using deep learning techniques - Interactive solutions for image restoration - Patch match image editing and synthesis - Re-colorization and art restoration from incomplete data
Last updated by Dou Sun in 2019-07-06
Special Issue on Biometrics in Smart Cities: Techniques and Applications (BI_SCI)
Submission Date: 2020-05-20

Smart Cities aim at improving the daily life of the citizens, by upgrading the services in terms of mobility, communications and power efficiency. Also, by measuring and acquiring the habits of a specific subject, it is meant to offer specific user-oriented services. In this scenario, biometric recognition covers a privileged role. Researchers worldwide agree with the fact that biometric credentials are difficult to be stolen and do not need to be remembered, so making them suitable for on-the-move authentication or video surveillance applications in smart cities environments. In order to be compliant with requirements of a really smart scenario, biometric recognition must be non-intrusive and transparent to the user, being also able to acquire data and information in ”in-the-wild” environment, thus providing a natural tool to customize the surrounding environment to user needs and attitudes. Strong biometric traits like fingerprint or palmprint may work a in very limited range of possible applications. Conversely, contactless biometric traits, and more in particular soft biometrics, still represent an unexplored field of research in this sense. The ever-increasing number of surveillance cameras everywhere and the unrestrainable growth in usage of wearable devices implies the number of facial images and video available will grow exponentially. These conditions pose the premises for a wide range of application fields dealing with tracking and pose estimation of people for gait and gender recognition, person re-identification in adverse and crowded scenes, biometric data fusion to improve recognition accuracy and so on. Recent deep neural network models have shown a significant improvement on still images and video sequences in these areas, but a wide set of open problems persists and to which solutions still have not been clearly defined. The objective of this virtual special issue is to solicit and publish the latest advances on this topic which includes, but are not limited to, the following: Contactless biometric recognition suitable for smart environments; Face recognition in the wild; Pose estimation and gait recognition; Person re-identification in surveillance; Voice recognition; Liveness/Spoofing Detection for biometric attacks; Continuous biometric authentication approaches; Cloud-based biometrics databases; Biometric Access Control to cloud services; Biometric Systems for Cloud-based Architectures; Mobile Biometrics and Cloud Computing; Cloud-based Communication Protocols for Biometric Systems; Biometric-based customized content presentation; Biometric Security in smart environments. Bio signal Security Bio signal Steganography Data Hiding
Last updated by Dou Sun in 2019-07-06
Special Issue on Cross-Media Learning for Visual Question Answering (VQA)
Submission Date: 2020-06-05

Visual Question Answering (VQA) is a recent hot topic which involves multimedia analysis, computer vision (CV), natural language processing (NLP), and even a broad perspective of artificial intelligence, which has attracted a large amount of interest from the deep learning, CV, and NLP communities. The definition of this task is shown as follows: a VQA system takes a picture and a free, open-ended question in the form of natural language about the picture as input and takes the generation of a piece of answer in the form of natural language as the output. It is required that pictures and problems should be taken as input of a VQA system, and a piece of human language is required to be generated as output by integrating information of these two parts. For a specific picture, if we want that the machine can answer a specific question about the picture in natural language, we need to enable the machine to have certain understanding of the content of the picture, and the meaning and intention of the question, as well as relevant knowledge. VQA relates to AI technologies in multiple aspects: fine-grained recognition, object recognition, behavior recognition, and understanding of the text contained in the question (NLP). Because VQA is closely related to the content both in CV and NLP, a natural QA solution is integrating CNN with RNN, which are successfully used in CV and NLP, to construct a composite model. To sum up, VQA is a learning task linked to CV and NLP. The task of VQA is rather challenging because it requires to comprehend textual questions, and analyze visual questions and image elements, as well as reasoning about these forms. Moreover, sometimes external or commonsense knowledge is required as the basis. Although some achievements have been made in VQA study currently, the overall accuracy rate is not high as far as the effect achieved by the current model is concerned. As the present VQA model is relatively simple in structure, single in the content and form of the answer, the correct answer is not so easy to obtain for the slightly complex questions which requires more prior knowledge for simple reasoning. Therefore, this Special Section in Journal of Visual Communication and Image Representation aims to solicit original technical papers with novel contributions on the convergence of CV, NLP and Deep Leaning, as well as theoretical contributions that are relevant to the connection between natural language and CV. Topics The topics of interest include, but are not limited to:  Deep learning methodology and its applications on VQA, e.g. human computer interaction, intelligent cross-media query and etc.  Image captioning indexing and retrieval  Deep Learning for big data discovery  Visual Relationship in VQA  Question Answering in Images  Grounding Language and VQA  Image target location using VQA  Captioning Events in Videos  Attention mechanism in VQA system  Exploring novel models and datasets for VQA
Last updated by Dou Sun in 2019-07-06
Special Issue on Advances in Human Action, Activity and Gesture Recognition (AHAAGR)
Submission Date: 2020-06-30

The goal of this Special Issue onAdvances on Human Action, Activity and Gesture Recognition (AHAAGR)is to gather the most contemporary achievements and breakthroughs in the fields of human action and activity recognition under one cover in order to help the research communities to set future goals in these areas by evaluating the current states and trends. Especially, due to the advancement of computational power and camera/sensor technology, deep learning, there has been a paradigm shift in video-based or sensor-based research in the last few years. Hence, it is of utmost importance to compile the accomplishments and reflect upon them to reach further. This issue is soliciting original & technically-sound research articles with theoretical & practical contributions from the computer vision, machine learning, imaging, robotics, & AI communities. Topics of interest include (but are not limited to): Human action/activities/gesture recognition from video or other relevant sensor data Large datasets on action/activity/gesture recognition Multi-sensor action/activity/gesture recognition Action/activity/gesture recognition from skeleton data, depth map. Deep learning and action recognition Action localization and detection; Action sequence generation/completion Anomaly detection from surveillance videos; Action recognition in Robotics Hand gesture recognition for virtual reality and other applications Crowd behavior analysis and prediction from video sequences Human behavior analysis and recognizing social interactions Behavior recognition based on bodily & facial expressions Applications and future trends of action/activity/gesture recognition
Last updated by Dou Sun in 2019-07-06
Special Issue on Implicit BIOmetric Authentication and Monitoring through Internet of Things
Submission Date: 2020-09-30

According to reliable forecasts, the expected number of connected IoT devices could exceed 25 billions by 2020. An important fraction of this number includes last generations mobile and wearable devices featuring an arsenal of advanced sensors (high speed/depth/multi-focal cameras, finger imaging, accelerometers, gyros, etc.), up to 5G communication capability and growing computing power. These collection of features makes them particularly suited to capture both static and dynamic biometrics, to continuosly monitor health signals and/or to provide information about the operating context. In summary, these capabilities will enable a new generation of Internet of Biometric Things (IoBT) approaches which will greatly extend the range and the target of "mainstream" biometric applications. This Special Issue aims at gathering the latest research findings and applications for transparent acquisition and processing of biometrics and health signals in the context of ubiquitous IoBT-based user authentication and monitoring, outlining new application scenarios for mobile biometrics. Topics include, but are not limited to: IoBT enabled biometrics Ubiquitous user authentication/recognition Ubiquitous biometric monitoring Implicit IoBT-enabled authentication/recognition Implicit IoBT-enabled activity recognition Implicit IoBT-enabled context detection Dynamic biometrics capture and processing Implicit psychophysical assessment Deep Learning for IoBT applications Health signals analysis via mobile devices Elders monitoring through IoBT devices and approaches Privacy and IoBT
Last updated by Dou Sun in 2019-10-20
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