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Open AccessArticle

Tensor Decomposition for Salient Object Detection in Images

1
Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
2
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
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Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2019, 3(2), 33; https://doi.org/10.3390/bdcc3020033
Received: 23 May 2019 / Revised: 14 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
The fundamental challenge of salient object detection is to find the decision boundary that separates the salient object from the background. Low-rank recovery models address this challenge by decomposing an image or image feature-based matrix into a low-rank matrix representing the image background and a sparse matrix representing salient objects. This method is simple and efficient in finding salient objects. However, it needs to convert high-dimensional feature space into a two-dimensional matrix. Therefore, it does not take full advantage of image features in discovering the salient object. In this article, we propose a tensor decomposition method which considers spatial consistency and tries to make full use of image feature information in detecting salient objects. First, we use high-dimensional image features in tensor to preserve spatial information about image features. Following this, we use a tensor low-rank and sparse model to decompose the image feature tensor into a low-rank tensor and a sparse tensor, where the low-rank tensor represents the background and the sparse tensor is used to identify the salient object. To solve the tensor low-rank and sparse model, we employed a heuristic strategy by relaxing the definition of tensor trace norm and tensor l1-norm. Experimental results on three saliency benchmarks demonstrate the effectiveness of the proposed tensor decomposition method. View Full-Text
Keywords: salient object; saliency detection; low-rank matrix recovery; tensor decomposition salient object; saliency detection; low-rank matrix recovery; tensor decomposition
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Zhou, J.; Tao, Y.; Liu, X. Tensor Decomposition for Salient Object Detection in Images. Big Data Cogn. Comput. 2019, 3, 33.

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