Info-Retrieval with Relevance Feedback using Hybrid Learning Scheme for RS Image

Zhou, Yonggang; Zeng, Zhi

doi:10.1109/cyberc.2019.00031

2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) 2019

DOI: 10.1109/cyberc.2019.00031

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Info-Retrieval with Relevance Feedback using Hybrid Learning Scheme for RS Image

Yonggang Zhou¹,

Zhi Zeng

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2023

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“…RS image retrieval got more attention, it has some limitations due to the scarcity of datasets and the complicated characteristics of RS image data. 4) Interactive RS image retrieval [27], [28], [29]: Experts in the image processing community employ active learning (AL) methods to iteratively boost the retrieval performance by taking the user's feedback into account [30]. AL leverages an expert oracle to label the most informative samples from the unlabeled archive.…”

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Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description

Ghozatlou

Conde

Datcu

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article studies remote sensing image retrieval using kernel-based support vector data description (SVDD). We exploit deep SVDD, which is a well-known method for one-class classification to recover the most relevant samples from the archive. To this end, a deep neural network (DNN) is jointly trained to map the data into a hypersphere of minimum volume in the latent space. It is expected that similar samples to the query are compressed inside of the hypersphere. The closest embedding to the center of the hypersphere is related to the most relevant sample to query. We enhance deep SVDD by injecting the statistical information of data to the DNN by means of additional terms in the cost function. The first enhancement method takes advantage of covariance regularization of batches of the training set to penalize unnecessary redundancy and minimize the correlation between the different dimensions of the embedding. The second method involves unlocking the hypersphere's predefined center while preventing network divergence during training. Therefore, two parameters are designed to control the importance of the drifting of the center and the importance of a fixed predefined center (convergence), respectively. This has been implemented by considering the average of batches of embedding in each iteration as the updated center. This pushes irrelevant samples away from query samples, making data clustering easier for the DNN. The performance of the proposed methods is evaluated on benchmark datasets.

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confidence: 99%

Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description

Ghozatlou

Conde

Datcu

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Info-Retrieval with Relevance Feedback using Hybrid Learning Scheme for RS Image

Cited by 1 publication

References 8 publications

Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description

Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description

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