Randomly Attracted Rough Firefly Algorithm for histogram based fuzzy image clustering

Dhal, Krishna Gopal; Das, Arunita; Ray, Swarnajit; Gálvez, Jorge

doi:10.1016/j.knosys.2021.106814

Cited by 58 publications

(17 citation statements)

References 24 publications

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“…These parameters are very well-known in image segmentation domain. Table 4 highlights the list of utilized segmentation quality parameters (Dhal et al 2020c , 2021b , 2021c ; Das et al 2021 ).…”

Section: Resultsmentioning

confidence: 99%

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Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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Section: Resultsmentioning

confidence: 99%

“…Exploration of multi-objective MLT can also be a great future work. Recently histogram clustering has been emerged as good alternative of histogram thresholding (Dhal et al 2021c ; Das et al 2021 ). Therefore, application of NIOA for histogram based image clustering can also be a good future direction.…”

Section: Discussionmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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“…But unlike the usual networks, PCANet's filters are more computationally efficient. PCANet is not obtained through training, but through obtaining the mapping results of parts of high-resolution film and television animation video images, and then adopts the PCA (principal component analysis) method to extract the principal components of the high-resolution film and television animation video images, each of which is an independent filter [16]. When the neural network of the filter is used to extract the features, the optimal weights can be obtained without iterative operation; thus, the calculation time is reduced [17].…”

Section: Deep Feature Extraction Model Based On Deep Learningmentioning

confidence: 99%

Animation Design Based on 3D Visual Communication Technology

Shan

Wang

2022

Scientific Programming

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The depth synthesis of image texture is neglected in the current image visual communication technology, which leads to the poor visual effect. Therefore, the design method of film and TV animation based on 3D visual communication technology is proposed. Collect film and television animation videos through 3D visual communication content production, server processing, and client processing. Through stitching, projection mapping, and animation video image frame texture synthesis, 3D vision conveys animation video image projection. In order to ensure the continuous variation of scaling factors between adjacent triangles of animation and video images, the scaling factor field is constructed. Deep learning is used to extract the deep features and to reconstruct the multiframe animated and animated video images based on visual communication. Based on this, the frame feature of video image under gray projection is identified and extracted, and the animation design based on 3D visual communication technology is completed. Experimental results show that the proposed method can enhance the visual transmission of animation video images significantly and can achieve high-precision reconstruction of video images in a short time.

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“…Development of HIOAs based image clustering especially histogram based image clustering should an emergent research topic [ 27 – 30 ]…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

Rai

Das

Ray

et al. 2022

Arch Computat Methods Eng

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Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms ( HIOAs ). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms ( HIOAs ) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

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Randomly Attracted Rough Firefly Algorithm for histogram based fuzzy image clustering

Cited by 58 publications

References 24 publications

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Animation Design Based on 3D Visual Communication Technology

Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

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