With the rapid development of information storage and networking technologies, quintillion
bytes of data are generated every day from social networks, business transactions, sensors …

During the last decade, the deluge of multimedia data has impacted a wide range of
research areas, including multimedia retrieval, 3D tracking, database management, data …

Nearest neighbor search is a problem of finding the data points from the database such that
the distances from them to the query point are the smallest. Learning to hash is one of the …

Recent vision and learning studies show that learning compact hash codes can facilitate
massive data processing with significantly reduced storage and computation. Particularly …

For many computer vision and machine learning problems, large training sets are key for
good performance. However, the most computationally expensive part of many computer …

The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved
great success for fast high-recall search, but are extremely expensive when handling very …

Recent years have witnessed the growing popularity of hashing in large-scale vision
problems. It has been shown that the hashing quality could be boosted by leveraging …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

This paper presents a novel compact coding approach, composite quantization, for
approximate nearest neighbor search. The idea is to use the composition of several …