Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

Abstract

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method.

Mixed Region Covariance Discriminative Learning for Image Classification on Riemannian Manifolds

“…The complexity of computing the covariance of rectangular region mainly focuses on the computation of region covariance. A fast computation of the region covariance descriptor can be realized based on the integral image [1,6], and the computational cost for any input image region is [ 2 ( + 1)]. Considering the properties of the LERM, we perform the matrix logarithm operate on each mixed region covariance matrix; it can be computed in [ 3 ] time.…”

“…Image classification is an important and prevalent topic in pattern recognition and computer vision research. Particularly when the object detecting [1] and visual tracking [2,3] are widely developed, these technologies have been effectively applied to numerous real world scenarios [4][5][6][7][8].…”

An Adaptive Denoising and Detection Approach for Underwater Sonar Image

“…The filtering degree parameter is a significant parameter. It is intensively related to denoising results [7]. The relatively large filtering degree parameter will cause the loss of some image detail information, especially the image edge information.…”

“…However, when the search window is small, the denoising ability of the non-local spatial information will be greatly reduced. Furthermore, the idea of threshold was introduced to remove the abnormal parameters and select the appropriate filtering degree parameters [7]. Nevertheless, the two thresholds are fixed values and only refer to one underwater sonar image.…”

“…To make the population more diverse and improve the search ability, Quantum computing theory is used to improve SFLA [24]. A quantum-inspired shuffled frog leaping algorithm combining the new search mechanism (QSFLA-NSM) is proposed to the underwater sonar image detection [7]. The experimental results of QSFLA-NSM show that it can further enhance population diversity and search ability.…”