Automatic salient object segmentation using saliency map and color segmentation

Han, Sung-Ho; Jung, Gye-dong; Lee, Sangh-yuk; Hong, Yeong-Pyo; Lee, Sang-Hun

doi:10.1007/s11771-013-1750-1

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…These approaches aim to compute a saliency map to define where the objects are located. For most methods , low‐level features, including color, intensity, contrast and so on, are important to find salient objects. Achanta et al .…”

Section: Introductionmentioning

confidence: 99%

GE‐RM: Efficient global estimation and refined model for salient object detection via elaborate receptive fields

Wang

Jiang

Wang

2018

IEEJ Transactions Elec Engng

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Recently, a deep learning technique has been introduced to saliency detection and has achieved promising results, but most of them are based on superpixel algorithms. Consequently, their performances and efficiencies depend largely on the results of a segmentation algorithm. Instead of classifying superpixels, we treat salient object detection as a dense prediction task. Fully convolutional networks show strong potential in dense prediction tasks, but the resolution and the quality of output maps need improving due to the loss of location information. In order to achieve high-quality saliency maps, we propose a very efficient method using two Fully Convolutional Networks (FCNs) to extract global and local information respectively via different receptive fields. The global model produces accurate but coarse saliency maps, while the refined model produces full-sized, fine results. We evaluate our method on eight public datasets and find that our method outperforms the other state-of-the-art methods. Besides, our method runs much faster than the existing deep learning methods.

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Section: Introductionmentioning

confidence: 99%

GE‐RM: Efficient global estimation and refined model for salient object detection via elaborate receptive fields

Wang

Jiang

Wang

2018

IEEJ Transactions Elec Engng

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“…Bottom‐up saliency for videos helps in foreground region detection by assigning a saliency measure to a pixel to be spatially and temporally salient. The most commonly used technique to easily segment the foreground from the background is by applying a threshold to the saliency map [1]. However, if the saliency map is not accurate, this might result in spurious regions being identified as foreground.…”

Section: Introductionmentioning

confidence: 99%

Particle filter framework for salient object detection in videos

Muthuswamy

Rajan

2015

IET Computer Vision

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Salient object detection in videos is challenging because of the competing motion in the background, resulting from camera tracking an object of interest, or motion of objects in the foreground. The authors present a fast method to detect salient video objects using particle filters, which are guided by spatio-temporal saliency maps and colour feature with the ability to quickly recover from false detections. The proposed method for generating spatial and motion saliency maps is based on comparing local features with dominant features present in the frame. A region is marked salient if there is a large difference between local and dominant features. For spatial saliency, hue and saturation features are used, while for motion saliency, optical flow vectors are used as features. Experimental results on standard datasets for video segmentation and for saliency detection show superior performance over state-of-the-art methods.

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