Journal Information
Pattern Recognition
http://www.journals.elsevier.com/pattern-recognition/
Impact Factor:
4.582
Publisher:
ELSEVIER
ISSN:
0031-3203
Viewed:
5119
Tracked:
16

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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 Dou Sun in 2017-08-05
Special Issues
Special Issue on Open-Set Big Data Understanding: Theory and Applications
Submission Date: 2017-12-01

Living in the era of big data, we have witnessed a dramatic growth of heterogeneous data, such as text, image, video, audio, graphics, and time series, emerging from surveillance, social media, wearable devices, IoT, and so on. The hybrid big data has posed new challenges in designing effective algorithms and generalized frameworks to meet the heterogeneous computing requirements. Although significant improvements have been achieved in diverse key areas, such as object detection, image classification, action recognition, event modeling, and scene parsing, a tremendous performance gap exists between the theoretical & laboratory evaluations and under real-world application scenarios. The key drawback is the inconsistency of class setting and data conditions between the training dataset and application scenario. In particular, this is due to two largely ignored problems: (1) open-view problem: the trained model is required to recognise a familiar class under drastically different environment; and (2) open-set problem: the trained model is required to reject an imposter from claiming the identity of one of the known class. In fact, it is not feasible to assume all potential classes can be enumerated during the training phase. Take action recognition as an example, how to deploy a lab-trained action recognition model with limited video samples to real-world surveillance environment without camera-specific re-tuning stage. To address this challenge, it is necessary to develop new theories and methods that are in contrast to the traditional frameworks which assume all classes are known during the training phase, and the test dataset exhibit similar conditions as the training dataset. This special issue seeks high-quality original research papers on (1) present state-of- the-art theories and novel application scenarios related to open-set big data understanding; (2) develop novel methods and applications; (3) survey the recent progress in this area; and (4) establish benchmark datasets. The topics include (but not limited to): 1.Theory ○ Data-driven feature learning ○ Cross-domain feature embedding ○ Zero-shot / One-shot learning ○ Transfer learning ○ Multi-task learning ○ Weakly supervised learning ○ Deep learning 2.Applications ○ Object detection / classification ○ Human action recognition ○ Video event identification ○ Multimedia retrieval and search ○ Vision and language ○ Name entity recognition ○ Question answering system ○ Biomedical image analysis ○ Novel dataset and benchmark for open domain big data analytics
Last updated by Dou Sun in 2017-08-05
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
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