Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

DeepDyve requires Javascript to function. Please enable Javascript on your browser to continue.

Visual Pattern Discovery and Recognition

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-10-4840-1

ISBN: 978-981-10-4839-5

Publisher: Springer Singapore - Springer Journals

Jun 16, 2017

Available

Introduction

Wang, Hongxing; Weng, Chaoqun; Yuan, Junsong

Pages: 1 –13

[As re-occurring compositions of visual data, visual patterns exist in complex spatial structures and diverse feature views of image and video data. Discovering visual patterns is of great interest to visual data analysis and recognition. Many methods have been proposed to address the problem of...
Available

Context-Aware Discovery of Visual Co-occurrence Patterns

Wang, Hongxing; Weng, Chaoqun; Yuan, Junsong

Pages: 15 –28

[Once images are decomposed into a number of visual primitives, it is of great interests to cluster these primitives into mid-level visual patterns. However, conventional clustering of visual primitives, e.g., bag-of-words, usually ignores the spatial context and multi-feature information among...
Available

Hierarchical Sparse Coding for Visual Co-occurrence Discovery

Wang, Hongxing; Weng, Chaoqun; Yuan, Junsong

Pages: 29 –44

[In this chapter, we investigate soft assignments instead of hard assignments used in Chap. 2 and propose a hierarchical sparse coding method to learn representative mid-level visual phrases. Given multiple types of low-level visual primitive features, we first learn their sparse codes,...
Available

Feature Co-occurrence for Visual Labeling

Wang, Hongxing; Weng, Chaoqun; Yuan, Junsong

Pages: 45 –65

[Due to the difficulties in obtaining labeled visual data, there has been an increasing interest to label a limited amount of data and then propagate the initial labels to a large amount of unlabeled data. In this chapter, we propose a transductive label propagation algorithm by leveraging the...
Available

Visual Clustering with Minimax Feature Fusion

Wang, Hongxing; Weng, Chaoqun; Yuan, Junsong

Pages: 67 –83

[To leverage multiple feature types for visual data analytics, various methods have been presented in Chaps. 2–4. However, all of them require the extra information, e.g., the spatial context information and the data label information. It is often difficult to obtain such information in practice....
Available

Conclusion

Wang, Hongxing; Weng, Chaoqun; Yuan, Junsong

Pages: 85 –87

[Over the past dozen years, visual pattern discovery has received increasing attention, especially in the communities of computer vision and data mining. This book provides a systematic study on the visual pattern discovery problems from unsupervised to semi-supervised manner approaches. This...

results per page

Prev
1
Next

Browse All eBooks