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Semantic image segmentation using region-based object detector
- Source :
- 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI).
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Semantic image segmentation plays a central role in image understanding and could be applied in various applications. With the development of deep learning techniques, great progress has been made in recent years. However, it is still very challenging to produce accurate segmentation results especially near object boundaries. Furthermore, all the methods with top performances are contributed by pixel-level annotations which needs expensive human effort and tremendous labeling time. In this paper, we explore a simple semantic segmentation approach using region-based object detector which only needs bounding box annotations. The main idea is using object detector to classify region proposals and then applying saliency detection method to segment such classified proposals. Experimental results on PASCAL VOC 2012 validation dataset show its comparable performance with fully supervised methods by masks.
- Subjects :
- Computer science
business.industry
Deep learning
Detector
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
02 engineering and technology
Pascal (programming language)
Image segmentation
010501 environmental sciences
01 natural sciences
Minimum bounding box
0202 electrical engineering, electronic engineering, information engineering
Object detector
020201 artificial intelligence & image processing
Segmentation
Artificial intelligence
business
computer
0105 earth and related environmental sciences
computer.programming_language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)
- Accession number :
- edsair.doi...........f9ebf51a96c14a7e2f8d4c8c773adf78