Saliency Detection Using Deep Features and Affinity-Based Robust Background Subtraction

Mehmood Nawaz

doi:10.1109/TMM.2020.3019688

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Saliency Detection Using Deep Features and Affinity-Based Robust Background Subtraction

2021-01-01

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Mehmood Nawaz *

Hong Yan (Hong Yan)

DOI：10.1109/TMM.2020.3019688

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Abstract

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Most existing saliency methods measure fore- ground saliency by using the contrast of a foreground region to its local context, or boundary priors and spatial compactness. These methods are not powerful enough to extract a precise salient region from noisy and cluttered backgrounds. To evaluate the contrast of salient and background regions effectively, we consider high-level features from both supervised and unsupervised methods. We propose an affinity-based robust background subtraction technique and maximum attention map using a pre-trained convolution neural network. This affinity-based technique uses pixel similarities to propagate the values of salient pixels among foreground and background regions and their union. The salient pixel value controls the foreground and background information by using multiple pixel affinities. The maximum attention map is derived from the convolution neural network using features of the Pooling and Relu layers. This method can detect salient regions from images that have noisy and cluttered backgrounds. Our experimental results demonstrate the effectiveness of the proposed approach on six different saliency data sets and benchmarks and show that it improves the quality of detection beyond current saliency detection methods.

Keywords:

Feature extraction

Saliency detection

Object detection

Image reconstruction

Image segmentation

Image color analysis

Neural networks

Attention map

background subtraction

salient region

affinity matrix

convolution neural network

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