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Duplicate Image Representation Based on Semi-Supervised Learning

Duplicate Image Representation Based on Semi-Supervised Learning

Ming Chen, Jinghua Yan, Tieliang Gao, Yuhua Li, Huan Ma
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 13
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781683180524|DOI: 10.4018/IJGHPC.301578
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MLA

Chen, Ming, et al. "Duplicate Image Representation Based on Semi-Supervised Learning." IJGHPC vol.14, no.1 2022: pp.1-13. http://doi.org/10.4018/IJGHPC.301578

APA

Chen, M., Yan, J., Gao, T., Li, Y., & Ma, H. (2022). Duplicate Image Representation Based on Semi-Supervised Learning. International Journal of Grid and High Performance Computing (IJGHPC), 14(1), 1-13. http://doi.org/10.4018/IJGHPC.301578

Chicago

Chen, Ming, et al. "Duplicate Image Representation Based on Semi-Supervised Learning," International Journal of Grid and High Performance Computing (IJGHPC) 14, no.1: 1-13. http://doi.org/10.4018/IJGHPC.301578

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Abstract

For duplicate image detection, the more advanced large-scale image retrieval systems in recent years have mainly used the Bag-of-Feature ( BoF ) model to meet the real-time. However, due to the lack of semantic information in the training process of the visual dictionary, BoF model cannot guarantee semantic similarity. Therefore, this paper proposes a duplicate image representation algorithm based on semi-supervised learning. This algorithm first generates semi-supervised hashes, and then maps the image local descriptors to binary codes based on semi-supervised learning. Finally, an image is represented by a frequency histogram of binary codes. Since the semantic information can be effectively introduced through the construction of the marker matrix and the classification matrix during the training process, semi-supervised learning can not only guarantee the metric similarity of the local descriptors, but also guarantee the semantic similarity. And the experimental results also show this algorithm has a better retrieval effect compared with traditional algorithms.

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