Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Korytkowski, Marcin; Šenkeřík, Roman; Scherer, Magdalena; Angryk, Rafal A.; Kordos, Mirosław; Siwocha, Agnieszka

doi:10.2478/jaiscr-2020-0005

Cited by 28 publications

(5 citation statements)

References 82 publications

(75 reference statements)

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“…However, this mode depends heavily on feature extraction. Korytkowski et al [28] provided a powerful version of the differential evolution algorithm with effective embedded mechanisms for strong exploration and preservation of the population diversity. However, such a condition is heavy maintenance required by CBIR systems.…”

Section: State Of the Artmentioning

confidence: 99%

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

2022

Teh. vjesn.

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A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small-and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 -Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small-and medium-scale CBIR systems with inadequate samples.

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Section: State Of the Artmentioning

confidence: 99%

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

2022

Teh. vjesn.

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“…As we mentioned, several dataset groups can be classified by employing machine learning. In this context, we can found image classification problem [28], electronic noise classification of bacterial food bones pathogens [29], urban management [30] and to mention some recently studies. Finally, in [31] the authors use the DBSCAN algorithm to make a binarization strategy and transform the continuous search space to a binary one.…”

Section: Related Workmentioning

confidence: 99%

A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

et al. 2021

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Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

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“…SURF is combined with bag-of-words [13] in [2] to retrieve and classify images. Some retrieval approaches are base on fuzzy sets [14,24,25], fuzzy rules are used to classify and fast retrieve images in database environments.…”

Section: Related Workmentioning

confidence: 99%

Detecting Visual Objects by Edge Crawling

Grycuk

Wojciechowski

Wei

et al. 2020

Journal of Artificial Intelligence and Soft Computing Research

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AbstractContent-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.

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Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Cited by 28 publications

References 82 publications

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Detecting Visual Objects by Edge Crawling

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