Deep supervised hashing takes prominent advantages of low storage cost, high
computational efficiency and good retrieval performance, which draws attention in the field …

With the continuous development of mobile devices, the number of images on the Internet
increases explosively. Hashing methods solve retrieval problems with large datasets by …

Dominant set clustering has been widely used to solve a variety of problems such as image
segmentation, video analysis, and image retrieval. However, the key problem of it is the …

With the rapid development of mobile Internet, the dimension of speech data is too high and
the space is complex. The existing speech retrieval algorithms can not meet the efficient …

In recent years, Hashing has become a popular technique used to support large-scale
image retrieval, due to its significantly reduced storage, high search speed and capability of …

In the real world, multi-modal data often appears in a streaming fashion, and there is a
growing demand for similarity retrieval from such non-stationary data, especially at a large …

Online hashing methods aim to update hash functions with newly arriving data streams,
which can process large-scale data online. To this end, most existing methods update …

Existing non-stationary hashing methods only assume the data environment is dynamic,
without considering the concept drift phenomenon. Meanwhile, these methods update hash …

Online hashing methods usually learn the hash functions online, aiming to efficiently adapt
to the data variations in the streaming environment. However, when the hash functions are …

Weighted Hamming distance, as a similarity measure between binary codes and binary
queries, provides superior accuracy in search tasks than Hamming distance. However, how …