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.

Big Visual Data Analysis

Part of the SpringerBriefs in Electrical and Computer Engineering Book Series

DOI: 10.1007/978-981-10-0631-9

ISBN: 978-981-10-0629-6

Publisher: Springer Singapore - Springer Journals

Feb 25, 2016

Available

Introduction

Chen, Chen; Ren, Yuzhuo; Kuo, C.-C. Jay

Pages: 1 –5

[Large-scale visual data understanding is a long-standing popular problem in the computer vision society. When more visual data become available, problems become more challenging to traditional approaches. In this chapter, we will briefly review three important research problems, indoor/outdoor...
Available

Scene Understanding Datasets

Chen, Chen; Ren, Yuzhuo; Kuo, C.-C. Jay

Pages: 7 –21

[Many visual dataset has been made public available for the researcher’s convenience. In this chapter, we will review the most important scene image understanding datasets. The introduction will reflect the key challenges and problems in current scene understanding researches.]
Available

Indoor/Outdoor Classification with Multiple Experts

Chen, Chen; Ren, Yuzhuo; Kuo, C.-C. Jay

Pages: 23 –63

[Indoor/outdoor classification is a fundamental step toward scene understanding. When data become more diverse, traditional approaches are not able to efficiently provide robust performance. In this chapter, we will firstly review the weakness of existing approaches and then propose a systematic...
Available

Outdoor Scene Classification Using Labeled Segments

Chen, Chen; Ren, Yuzhuo; Kuo, C.-C. Jay

Pages: 65 –92

[Categorize scene images into classes require semantic understanding of the content in the images. However, traditional approaches start from pixels or local rigid rectangle patches, which are sub-optimal to semantic segments. In this chapter, we will review the significance and problems of...
Available

Global-Attributes Assisted Outdoor Scene Geometric Labeling

Chen, Chen; Ren, Yuzhuo; Kuo, C.-C. Jay

Pages: 93 –120

[An approach that extracts global attributes from outdoor images to facilitate geometric layout labeling is investigated in this work. The proposed Global-attributes Assisted Labeling (GAL) system exploits both local features and global attributes. First, by following a classical framework, we...
Available

Conclusion and Future Work

Chen, Chen; Ren, Yuzhuo; Kuo, C.-C. Jay

Pages: 121 –122

[As a conclusion to the discussions in previous chapters, this chapter will summarize the significant improvident from the proposed system over the traditional approaches on large-scale visual data understanding problems. We will see great potential of proposed algorithms in the end of the brief....

results per page

Prev
1
Next

Browse All eBooks