Next Article in Journal
Peacekeeping Conditions for an Artificial Intelligence Society
Previous Article in Journal
Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications
Open AccessArticle

Tensor Decomposition for Salient Object Detection in Images

Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2019, 3(2), 33;
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
Show Figures

Figure 1

MDPI and ACS Style

Zhou, J.; Tao, Y.; Liu, X. Tensor Decomposition for Salient Object Detection in Images. Big Data Cogn. Comput. 2019, 3, 33.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop