Fast Semantic Preserving Hashing for LargeScale CrossModal Retrieval Most Crossmodal hashing methods do not sufficiently exploit the discrimination power of semantic information when learning hash codes, while often involving timeconsuming training procedures for largescale dataset. To tackle these issues, we first formulate the learning of similaritypreserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to largescale crossmodal retrieval. Specifically, FSePH introduces an orthonormal basis to regress the targeted hash codes of training examples to their corresponding reasonably relaxed class labels, featuring significantly reducing the quantization error. Meanwhile, an effective optimization algorithm is derived for modalityspecific projection function learning and an efficient closedform solution for hash code learning, which are computationally tractable. Extensive experiments have shown that the proposed FSePH approach runs sufficiently fast, and also significantly improves the retrieval performances over the stateofthearts.

CrossModal ZeroShot Hashing Hashing has been widely studied for big data retrieval due to its low storage cost and fast query speed. Zeroshot hashing (ZSH) aims to learn a hashing model that is trained using only samples from seen categories, but can generalize well to samples of unseen categories. ZSH generally uses category attributes to seek a semantic embedding space to transfer knowledge from seen categories to unseen ones. As a result, it may perform poorly when labeled data are insufficient. ZSH methods are mainly designed for singlemodality data, which prevents their application to the widely spread multimodal data. On the other hand, existing crossmodal hashing solutions assume that all the modalities share the same category labels, while in practice the labels of different data modalities may be different. To address these issues, we propose a general Crossmodal Zeroshot Hashing (CZHash) solution to effectively leverage unlabeled and labeled multimodality data with different label spaces. CZHash first quantifies the composite similarity between instances using label and feature information. It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning. CZHash further introduces an alternative optimization procedure to jointly optimize these learning objectives. Experiments on benchmark multimodal datasets show that CZHash significantly outperforms related representative hashing approaches both on effectiveness and adaptability.