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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 …