Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval

Magliani, Federico; Sani, Laura; Cagnoni, Stefano; Prati, Andrea

doi:10.1145/3349801.3349815

Cited by 13 publications

(9 citation statements)

References 35 publications

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“…In this paper, we use a genetic algorithm to optimize the diffusion process applied to image retrieval. The diffusion parameter tuning described in this paper extends the work we presented in [ 46 ], where only preliminary tests were reported. We include many more details on the proposed solution and describe the results of an extensive experimentation on several public datasets for CBIR by which we could demonstrate that the introduction of diffusion and the optimization of its parameters significantly improve retrieval quality.…”

Section: Related Workmentioning

confidence: 76%

“…To reduce the time required by this process, we built an approximate kNN graph instead of using an exhaustive approach to create a full lossless graph. As demonstrated in [ 46 ] and, more extensively, in Section 4 , the introduction of the diffusion process can produce significant improvements of retrieval quality. However, the performance of diffusion is sensitive to the setting of its parameters.…”

Section: The Overall Architecturementioning

confidence: 99%

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Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Magliani

Sani

Cagnoni

et al. 2020

Sensors

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Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.

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Section: Related Workmentioning

confidence: 76%

Section: The Overall Architecturementioning

confidence: 99%

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Magliani

Sani

Cagnoni

et al. 2020

Sensors

Self Cite

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“…Genetic algorithms are not fully suitable for the considered task of ensuring the SRCPS's information security, since they are aimed at solving optimization tasks, while in counteracting attacks the system requires not an optimal solution, but any of the algorithms suitable for restoring the target function. However, genetic algorithms provide high speed and have versatility, according to [7,8]. For example, in [8], the authors analyzed the results of different optimization techniques and noted that "The genetic algorithms achieve an excellent result in much shorter time than the others.…”

Section: Related Workmentioning

confidence: 99%

“…However, genetic algorithms provide high speed and have versatility, according to [7,8]. For example, in [8], the authors analyzed the results of different optimization techniques and noted that "The genetic algorithms achieve an excellent result in much shorter time than the others. It is to be noticed that, in all the previous experiments, the genetic algorithm has performed better than manual configuration and random search".…”

Section: Related Workmentioning

confidence: 99%

Cyber Attack Prevention Based on Evolutionary Cybernetics Approach

et al. 2020

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The paper looks at the problem of cybersecurity in modern cyber–physical systems and proposes an evolutionary model approach to counteract cyber attacks by self-regulating the structure of the system, as well as several evolutionary indicators to assess the state of the system. The application of evolutionary models makes it possible to describe the regularities of systems behavior and their technical development, which is especially important regarding cyber attacks, which are the cause of a discontinuous evolution of complex systems. A practical example describes a system behavior during attacks and the self-regulation of its structure. The methodological approach consists of using evolutionary models to describe how modern cyber–physical systems can counteract cyber attacks and evolve, building on the experience of past security incidents. The main conclusions and recommendations are presented in the Discussion section, and they consist of the fact that using an evolutionary approach will not only increase the security of cyber–physical systems, but also define the principles of building systems that are resistant to cyber attacks.

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“…The nature-inspired approaches are further segmented into evolutionary algorithms (EA) and swarm intelligence. The most widely used EA approach is genetic algorithm (GA) [37].…”

Section: Swarm Intelligence Overview and Cloud Computing Applicationsmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval

Cited by 13 publications

References 35 publications

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Cyber Attack Prevention Based on Evolutionary Cybernetics Approach

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

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