Journal Information
Pattern Recognition
http://www.journals.elsevier.com/pattern-recognition/
Impact Factor:
4.582
Publisher:
ELSEVIER
ISSN:
0031-3203
Viewed:
5853
Tracked:
19

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Call For Papers
Pattern Recognition is the official journal of the Pattern Recognition Society. The Society was formed to fill a need for information exchange among research workers in the pattern recognition field. Up to now, we ''pattern-recognitionophiles'' have been tagging along in computer science, information theory, optical processing techniques, and other miscellaneous fields. Because this work in pattern recognition presently appears in widely spread articles and as isolated lectures in conferences in many diverse areas, the purpose of the journal Pattern Recognition is to give all of us an opportunity to get together in one place to publish our work. The journal will thereby expedite communication among research scientists interested in pattern recognition.

We consider pattern recognition in the broad sense, and we assume that the journal will be read by people with a common interest in pattern recognition but from many diverse backgrounds. These include biometrics, target recognition, biological taxonomy, meteorology, space science, classification methods, character recognition, image processing, industrial applications, neural computing, and many others.

The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition.
Last updated by Feng Li in 2017-08-24
Special Issues
Special Issue on Advances in Representation Learning
Submission Date: 2018-03-01

Representation learning has always been an important research area in pattern recognition. A good representation of practical data is critical to achieving satisfactory recognition performance. Broadly speaking, such presentation can be ``intra-data representation’’ or ``inter-data representation’’. Intra-data representation focuses on extracting or refining the raw feature of data point itself. Representative methods range from the early-staged hand-crafted feature design (e.g. SIFT, LBP, HoG, etc.), to the feature extraction (e.g. PCA, LDA, LLE, etc.) and feature selection (e.g. sparsity-based and submodulariry-based methods) in the past two decades, until the recent deep neural networks (e.g. CNN, RNN, etc.). Inter-data representation characterizes the relationship between different data points or the structure carried out by the dataset. For example, metric learning, kernel learning and causality reasoning investigate the spatial or temporal relationship among different examples, while subspace learning, manifold learning and clustering discover the underlying structural property inherited by the dataset. Above analyses reflect that representation learning covers a wide range of research topics related to pattern recognition. On one hand, many new algorithms on representation learning are put forward every year to cater for the needs of processing and understanding various practical data. On the other hand, massive problems regarding representation learning still remain unsolved, especially for the big data and noisy data. Thereby, the objective of this special issue is to provide a stage for researchers all over the world to publish their latest and original results on representation learning. Topics of interest include, but are not limited to: - Unsupervised, semi-supervised, and supervised representation learning - Metric learning and kernel learning - Sparse representation and coding - Manifold learning, subspace learning and dimensionality reduction - Deep learning and hierarchical models - Optimization for representation learning - Probabilistic Graphical Models - Multi-view/Multi-modal learning - Representation learning for planning and reinforcement learning - Applications of representation learning
Last updated by Dou Sun in 2017-08-05
Special Issue on Bio/Neuroscience inspired pattern recognition
Submission Date: 2018-03-31

The general question addressed by the special issue is the latest research results obtained through the interaction of bio / neuroscience and pattern recognition fields benefitting both research areas. The fundamental point of the special issue is to study and investigate how bio / neuroscience inspired systems, including hardware and software, deal with problems directly related to pattern recognition (e.g., deep learning, representation learning, transfer learning, multi-task learning, and unsupervised learning, spike neural network). We seek to include in the special issue recent successful studies on pattern recognition incorporating ideas and paradigms from the field of neuroscience. We also seek contributions from where neuroscience-inspired algorithms for pattern recognition still fall behind the state-of-the-art in terms of speed and accuracy. We also cover areas where deeper connections are likely to be fruitful. For example, we would like to highlight how neuroscience driven simulations (either hardware or software based) suggest new directions, which offer real advances for pattern recognition. Note that we are not interested in papers that focus on the details of such hardware or software, but on how they simulate pattern recognition, based on biological and neuro-scientific principles. The submission of papers making fundamental or practical contributions to the following topics is encouraged, but not necessarily limited to these topics: - deeper connections : between bio/neuroscience and pattern recognition - latest research results - new bio/neuroscience paradigms - Technical issues: representation learning, deep learning, transfer learning, multi-task learning unsupervised learning, spike neural network - fundamental issues
Last updated by Dou Sun in 2017-09-30
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