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Learning Label Preserving Binary Codes for Multimedia Retrieval: A General Approach
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DOI：10.1145/3152126.png)
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Abstract
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Learning-based hashing has been researched extensively in the past few years due to its great potential in fast and accurate similarity search among huge volumes of multimedia data. In this article, we present a novel multimedia hashing framework, called Label Preserving Multimedia Hashing (LPMH) for multimedia similarity search. In LPMH, a general optimization method is used to learn the joint binary codes of multiple media types by explicitly preserving semantic label information. Compared with existing hashing methods which are typically developed under and thus restricted to some specific objective functions, the proposed optimization strategy is not tied to any specific loss function and can easily incorporate bit balance constraints to produce well-balanced binary codes. Specifically, our formulation leads to a set of Binary Integer Programming (BIP) problems that have exact solutions both with and without bit balance constraints. These problems can be solved extremely fast and the solution can easily scale up to large-scale datasets. In the hash function learning stage, the boosted decision trees algorithm is utilized to learn multiple media-specific hash functions that can map heterogeneous data sources into a homogeneous Hamming space for cross-media retrieval. We have comprehensively evaluated the proposed method using a range of large-scale datasets in both single-media and cross-media retrieval tasks. The experimental results demonstrate that LPMH is competitive with state-of-the-art methods in both speed and accuracy.
Keywords:
Learning to hash
supervised learning
multimedia retrieval
large-scale similarity search
discrete optimization
binary integer programming
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