Saliency Detection: A Boolean Map Approach
Jianming Zhang,Stan Sclaroff +1 more
- 01 Dec 2013
- pp 153-160
TL;DR: A novel Boolean Map based Saliency model, based on a Gestalt principle of figure-ground segregation, that consistently achieves state-of-the-art performance compared with ten leading methods on five eye tracking datasets.
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Abstract: A novel Boolean Map based Saliency (BMS) model is proposed. An image is characterized by a set of binary images, which are generated by randomly thresholding the image's color channels. Based on a Gestalt principle of figure-ground segregation, BMS computes saliency maps by analyzing the topological structure of Boolean maps. BMS is simple to implement and efficient to run. Despite its simplicity, BMS consistently achieves state-of-the-art performance compared with ten leading methods on five eye tracking datasets. Furthermore, BMS is also shown to be advantageous in salient object detection.
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Citations
Salient Object Detection: A Benchmark
TL;DR: In this article, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) were evaluated over 6 challenging datasets for the purpose of benchmarking salient object detector and segmentation methods.
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Salient Object Detection: A Discriminative Regional Feature Integration Approach
Huaizu Jiang,Jingdong Wang,Zejian Yuan,Yang Wu,Nanning Zheng,Shipeng Li +5 more
- 23 Jun 2013
TL;DR: This paper regards saliency map computation as a regression problem, which is based on multi-level image segmentation, and uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the salency map.
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Salient Object Detection: A Benchmark
TL;DR: It is found that the models designed specifically for salient object detection generally work better than models in closely related areas, which provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems.
Saliency detection by multi-context deep learning
Rui Zhao,Wanli Ouyang,Hongsheng Li,Xiaogang Wang +3 more
- 07 Jun 2015
TL;DR: This paper proposes a multi-context deep learning framework for salient object detection that employs deep Convolutional Neural Networks to model saliency of objects in images and investigates different pre-training strategies to provide a better initialization for training the deep neural networks.
SALICON: Saliency in Context
Ming Jiang,Shengsheng Huang,Juanyong Duan,Qi Zhao +3 more
- 07 Jun 2015
TL;DR: A mouse-contingent multi-resolutional paradigm based on neurophysiological and psychophysical studies of peripheral vision, to simulate the natural viewing behavior of humans is designed, thus enabling large-scale data collection.
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