[Paper] Image Retrieval Based on Supervised Local Regression and Global Alignment with Relevance Feedback for Insect Identification

Maeda, Kazuo; Susumu, Genma; Ogawa, Takahiro; Haseyama, Miki

doi:10.3169/mta.8.140

Cited by 1 publication

(1 citation statement)

References 40 publications

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“…Therefore, Yin et al [21] combined improved decision-directed algorithm with information entropy to enhance the performance of Kmeans, but obtaining sufficient computing power is difficult. Maeda et al [22] suggested a method for insect image retrieval based on supervised local regression and global alignment with relevance feedback. Their approach estimates ranking scores by preserving the neighborhood structure of feature space.…”

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.

View full text Add to dashboard Cite

show abstract

[Paper] Image Retrieval Based on Supervised Local Regression and Global Alignment with Relevance Feedback for Insect Identification

Cited by 1 publication

References 40 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

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