Sequential Compact Code Learning for Unsupervised Image Hashing

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@Article{Liu:2015:ieeeNNLS,

• author = "Li Liu and Ling Shao",
• journal = "IEEE Transactions on Neural Networks and Learning Systems",
• title = "Sequential Compact Code Learning for Unsupervised Image Hashing",
• year = "2015",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2162-237X",
• DOI = "doi:10.1109/TNNLS.2015.2495345",
• abstract = "Effective hashing for large-scale image databases is a popular research area, attracting much attention in computer vision and visual information retrieval. Several recent methods attempt to learn either graph embedding or semantic coding for fast and accurate applications. In this paper, a novel unsupervised framework, termed evolutionary compact embedding (ECE), is introduced to automatically learn the task-specific binary hash codes. It can be regarded as an optimisation algorithm that combines the genetic programming (GP) and a boosting trick. In our architecture, each bit of ECE is iteratively computed using a weak binary classification function, which is generated through GP evolving by jointly minimizing its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimisation by embedding high-dimensional data points into a similarity-preserved Hamming space with a low dimension. We systematically evaluate ECE on two data sets, SIFT 1M and GIST 1M, showing the effectiveness and the accuracy of our method for a large-scale similarity search.",
• notes = "Also known as \cite{7323857}",

}

Genetic Programming entries for Li Liu Ling Shao

Citations