A Swarm Intelligence inspired algorithm for contour detection in images

Kirchmaier, Ulrich; Hawe, Simon; Diepold, Klaus

doi:10.1016/j.asoc.2012.07.017

Cited by 14 publications

(6 citation statements)

References 34 publications

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“…These two pieces of information, which correspond to cognitive and social learning, respectively, lead the swarm population to find the best possible solution 12 . Although these two nature‐based algorithms, ACO and PSO, have been applied for solving a wide range of problems, 13,14 the most widely studied organism in swarm intelligence is the honey bee 15 . Abbass 16 first proposed Honey Bee Mating Optimization (HBMO) in Reference 16 and applied it to solve propositional satisfiability problems (3‐SAT problems).…”

Section: Related Workmentioning

confidence: 99%

Toward text psychology analysis using social spider optimization algorithm

Hosseinalipour

Gharehchopogh

Màsdàrí

et al. 2021

Concurrency and Computation

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Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi‐objective opposition‐based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi‐objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C‐MEANS data clustering technique, a decision tree (DT), and Naïve Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA‐ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.

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Section: Related Workmentioning

confidence: 99%

Toward text psychology analysis using social spider optimization algorithm

Hosseinalipour

Gharehchopogh

Màsdàrí

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Particle swarm optimization (PSO) is a standard and widely used evolutionary optimization algorithm for various applications [49]. PSO is very efficient in reaching an optimal solution utilizing limited memory, and faster computation [50] with 'primitive mathematical operations' [51]. Moreover, the optimization algorithm does not require any prior information about the solution.…”

Section: Part 2: Parameter Optimizationmentioning

confidence: 99%

Across-scale process similarity based interpolation for image super-resolution

Dhara

Sen

2019

Applied Soft Computing

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A pivotal step in image super-resolution techniques is interpolation, which aims at generating high resolution images without introducing artifacts such as blurring and ringing. In this paper, we propose a technique that performs interpolation through an infusion of high frequency signal components computed by exploiting 'process similarity'. By 'process similarity', we refer to the resemblance between a decomposition of the image at a resolution to the decomposition of the image at another resolution. In our approach, the decompositions generating image details and approximations are obtained through the discrete wavelet (DWT) and stationary wavelet (SWT) transforms. The complementary nature of DWT and SWT is leveraged to get the structural relation between the input image and its low resolution approximation. The structural relation is represented by optimal model parameters obtained through particle swarm optimization (PSO). Owing to process similarity, these parameters are used to generate the high resolution output image from the input image. The proposed approach is compared with six existing techniques qualitatively and in terms of PSNR, SSIM, and FSIM measures, along with computation time (CPU time).It is found that our approach is the fastest in terms of CPU time and produces comparable results. Recently, few end to end deep neural networks have been proposed for superresolution. SRCNN [30, 31] uses the convolutional neural network (CNN) [32] for image super-resolution. The technique is later extended to DRCN [9] by increasing the number of layers of the network and incorporating recursive learning.The techniques proposed in [8,11] use residual learning in deep and very deep If the aforesaid wavelet based analysis and synthesis is considered for interpolation, then, the input low resolution image can be decomposed into its immediate lower resolution approximation (LL) using (2) and high frequency details using (3). Now, one can assume that only the lower approximation is available for synthesizing back the input image, and not the high frequency details. In such a case, one can try to estimate the unavailable high frequency

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“…While most of the swarm intelligence algorithms have been employed in optimization tasks, works such as [8] and [16] have applied swarm-based solutions for image processing problems such as contour detection and three-dimensional reconstruction, respectively.…”

Section: B Swarm Intelligencementioning

confidence: 99%

“…These algorithms are based on the collective intelligence of relatively simple agents and have drawn attention due to their flexibility and robustness [6]. Swarmbased algorithms have been used to solve problems ranging from optimization [7] to image processing [8] tasks.…”

Section: Introductionmentioning

confidence: 99%

Swarm-based algorithm for phase unwrapping

Maciel

Albertazzi

2014

Appl. Opt.

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A novel algorithm for phase unwrapping based on swarm intelligence is proposed. The algorithm was designed based on three main goals: maximum coverage of reliable information, focused effort for better efficiency, and reliable unwrapping. Experiments were performed, and a new agent was designed to follow a simple set of five rules in order to collectively achieve these goals. These rules consist of random walking for unwrapping and searching, ambiguity evaluation by comparing unwrapped regions, and a replication behavior responsible for the good distribution of agents throughout the image. The results were comparable with the results from established methods. The swarm-based algorithm was able to suppress ambiguities better than the flood-fill algorithm without relying on lengthy processing times. In addition, future developments such as parallel processing and better-quality evaluation present great potential for the proposed method.

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A Swarm Intelligence inspired algorithm for contour detection in images

Cited by 14 publications

References 34 publications

Toward text psychology analysis using social spider optimization algorithm

Toward text psychology analysis using social spider optimization algorithm

Across-scale process similarity based interpolation for image super-resolution

Swarm-based algorithm for phase unwrapping

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