CFP: Pattern Recognition (PR) Special Issue on Compositional Models and Structur
CFP: Pattern Recognition Special Issue on Compositional Models and Structured Learning for Visual RecognitionThe issue will be aimed at accepting papers on the following topics but not limited to:
1. Object recognition and detection by learning and inference with compositional and hierarchical models. The proposed approaches are encouraged to evaluate on several public benchmarks in computer vision such as PASCAL VOC, ImageNet, Caltech101, and Caltech256.
2. Image segmentation and labeling with supervised or unsupervised learning methods, which incorporate multiple contextual object models. Some exemplar benchmarks are LabelMe, PASCAL VOC, Fashionista, and SUN databases.
3. Understanding human actions or activities from videos with spatio-temporal models. The new models will show good performance on capturing well large category variations that is one key challenge in complex action/activity modeling. By using depth sensors, more rich information can be utilized for these tasks.
4. Models, algorithms, and applications of sparse representation and dictionary learning. The proposed approaches are expected to improve the efficiency and effectiveness of the classification performance, and provide new insight for modeling structure and dependencies between vocabularies.
5. New applications and systems address real challenges in the intelligent processing and understanding of visual data (e.g. fashion understanding, medical image recognition, graphics, etc).
The main timelines for this issue are set as follows,
Paper submission due: July. 30, 2015
First notification: Nov. 30, 2015
Revision: Jan. 15, 2016
Final decision: Feb. 30, 2016
Submission Details:
All submissions for this special issue are required to follow the same format as regular full-length Pattern Recognition papers. The submission website for this special issue is located at: http://ees.elsevier.com/pr/. Please ensure to select 'SI : CHM-Vision' as the 'Article Type'.
Mark， 这个信息很重要！
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