4,333 research outputs found
HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces
Nearest neighbor searching of large databases in high-dimensional spaces is
inherently difficult due to the curse of dimensionality. A flavor of
approximation is, therefore, necessary to practically solve the problem of
nearest neighbor search. In this paper, we propose a novel yet simple indexing
scheme, HD-Index, to solve the problem of approximate k-nearest neighbor
queries in massive high-dimensional databases. HD-Index consists of a set of
novel hierarchical structures called RDB-trees built on Hilbert keys of
database objects. The leaves of the RDB-trees store distances of database
objects to reference objects, thereby allowing efficient pruning using distance
filters. In addition to triangular inequality, we also use Ptolemaic inequality
to produce better lower bounds. Experiments on massive (up to billion scale)
high-dimensional (up to 1000+) datasets show that HD-Index is effective,
efficient, and scalable.Comment: PVLDB 11(8):906-919, 201
Approximate Nearest Neighbor Fields in Video
We introduce RIANN (Ring Intersection Approximate Nearest Neighbor search),
an algorithm for matching patches of a video to a set of reference patches in
real-time. For each query, RIANN finds potential matches by intersecting rings
around key points in appearance space. Its search complexity is reversely
correlated to the amount of temporal change, making it a good fit for videos,
where typically most patches change slowly with time. Experiments show that
RIANN is up to two orders of magnitude faster than previous ANN methods, and is
the only solution that operates in real-time. We further demonstrate how RIANN
can be used for real-time video processing and provide examples for a range of
real-time video applications, including colorization, denoising, and several
artistic effects.Comment: A CVPR 2015 oral pape
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
- …