Learning to Detect a Salient Object
Top Cited Papers
- 31 January 2011
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Ieee Transactions On Pattern Analysis and Machine Intelligence
- Vol. 33 (2), 353-367
- https://doi.org/10.1109/TPAMI.2010.70
Abstract
In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. Further, we extend the proposed approach to detect a salient object from sequential images by introducing the dynamic salient features. We collected a large image database containing tens of thousands of carefully labeled images by multiple users and a video segment database, and conducted a set of experiments over them to demonstrate the effectiveness of the proposed approach.This publication has 33 references indexed in Scilit:
- Beyond sliding windows: Object localization by efficient subwindow searchPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Interesting objects are visually salientJournal of Vision, 2008
- Video retargetingPublished by Association for Computing Machinery (ACM) ,2006
- Gaze-based interaction for semi-automatic photo croppingPublished by Association for Computing Machinery (ACM) ,2006
- AutoCollagePublished by Association for Computing Machinery (ACM) ,2006
- TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and SegmentationLecture Notes in Computer Science, 2006
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Integral histogram: a fast way to extract histograms in Cartesian spacesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Learning to detect natural image boundaries using local brightness, color, and texture cuesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2004
- Mean shift: a robust approach toward feature space analysisIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002