Hashing is an efficient method for nearest neighbor search in large-scale data space by
embedding high-dimensional feature descriptors into a similarity preserving Hamming …

Clustering is a long-standing important research problem, however, remains challenging
when handling large-scale image data from diverse sources. In this paper, we present a …

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …

Multiview data clustering attracts more attention than their single-view counterparts due to
the fact that leveraging multiple independent and complementary information from multiview …

With the explosive growth of multimedia contents, multimedia retrieval is facing
unprecedented challenges on both storage cost and retrieval speed. Hashing technique can …

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in
which queries are abstract and ambiguous sketches while the retrieval database is formed …

Learning-based hashing algorithms are “hot topics” because they can greatly increase the
scale at which existing methods operate. In this paper, we propose a new learning-based …

Multi-modal hashing is an effective technique to support large-scale multimedia retrieval,
due to its capability of encoding heterogeneous multi-modal features into compact and …

Hashing could significantly accelerate large-scale image search by transforming the high-
dimensional features into binary Hamming space, where efficient similarity search can be …

Hashing plays a pivotal role in nearest-neighbor searching for large-scale image retrieval.
Recently, deep learning-based hashing methods have achieved promising performance …