Top-Down saliency by multi-scale contextual pooling

Authors:
Yuanyuan Qiu;Jun Zhu;Rui Zhang;Jun Huang
Affiliations:
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao T ...;Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao T ...;Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao T ...;Shanghai Advanced Research Institute, Chinese Academy of Sciences, China
Venue:
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Year:
2012

Citing 9
Cited 1

A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Visual attention detection in video sequences using spatiotemporal cues

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Discriminant Saliency, the Detection of Suspicious Coincidences, and Applications to Visual Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect a Salient Object

IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Goal-directed search with a top-down modulated computational attention system

PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Global contrast based salient region detection

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition

Top-Down Saliency Detection via Contextual Pooling

Journal of Signal Processing Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Goal-driven top-down mechanism plays an important role in the case of object detection and recognition. In this paper, we propose a top-down computational model for goal-driven saliency detection based on a coding-based classification framework. It consists of four successive steps: feature extraction, descriptor coding, local pooling and saliency prediction. In the step of local pooling, we investigate the effect of multi-scale contextual information for saliency detection and find that there exists an optimal contextual scale to achieve the patch-level feature presentation. On basis of this observation, we propose an approach for automatic scale selection in saliency prediction step. The experimental results demonstrate that our method can effectively improve the performance of goal-driven saliency detection as well as related object detection.