A balanced semi-supervised hashing method for CBIR

Jian-hui, Zhou; Fu, Haiyan; Kong, Xiangwei

doi:10.1109/icip.2011.6116164

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Semi-supervised Hashing1

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Cited by 10 publications

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“…However, supervised hashing methods require a fully labeled database is not realistic and difficult to scale up to large scale image retrieval problems. The Semi-supervised Hashing (SSH) is proposed to deal with semantic image retrieval problems with labels of some images in the database [15,16,17,18]. The SSH is trained using database with both labeled and unlabeled images which provides a flexibility on the databases usage.…”

Section: Semi-supervised Hashingmentioning

confidence: 99%

Weighted grid Principal Component Analysis hashing

Xian-cheng

Huang

2014

2014 International Conference on Machine Learning and Cybernetics

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Principal Component Analysis (PCA) is one of the most widely used components of hashing. In this paper, we propose three PCA-based hashing methods to improve the performance of the Principal Component Hashing (PCH). Different principal components have different among of variances of data. In the PCH, each principal component corresponds to a hash function. Hence, the PCH treats each principal component to have the same importance which will lead to the loss of much information in constructing hashing table. To deal with this shortage, we propose the weighted PCH (WPCH), the grid PCH (GPCH) and the weighted grid PCH (WGPCH).

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Section: Semi-supervised Hashingmentioning

confidence: 99%