4,308 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
Indexing the Earth Mover's Distance Using Normal Distributions
Querying uncertain data sets (represented as probability distributions)
presents many challenges due to the large amount of data involved and the
difficulties comparing uncertainty between distributions. The Earth Mover's
Distance (EMD) has increasingly been employed to compare uncertain data due to
its ability to effectively capture the differences between two distributions.
Computing the EMD entails finding a solution to the transportation problem,
which is computationally intensive. In this paper, we propose a new lower bound
to the EMD and an index structure to significantly improve the performance of
EMD based K-nearest neighbor (K-NN) queries on uncertain databases. We propose
a new lower bound to the EMD that approximates the EMD on a projection vector.
Each distribution is projected onto a vector and approximated by a normal
distribution, as well as an accompanying error term. We then represent each
normal as a point in a Hough transformed space. We then use the concept of
stochastic dominance to implement an efficient index structure in the
transformed space. We show that our method significantly decreases K-NN query
time on uncertain databases. The index structure also scales well with database
cardinality. It is well suited for heterogeneous data sets, helping to keep EMD
based queries tractable as uncertain data sets become larger and more complex.Comment: VLDB201
Ptolemaic Indexing
This paper discusses a new family of bounds for use in similarity search,
related to those used in metric indexing, but based on Ptolemy's inequality,
rather than the metric axioms. Ptolemy's inequality holds for the well-known
Euclidean distance, but is also shown here to hold for quadratic form metrics
in general, with Mahalanobis distance as an important special case. The
inequality is examined empirically on both synthetic and real-world data sets
and is also found to hold approximately, with a very low degree of error, for
important distances such as the angular pseudometric and several Lp norms.
Indexing experiments demonstrate a highly increased filtering power compared to
existing, triangular methods. It is also shown that combining the Ptolemaic and
triangular filtering can lead to better results than using either approach on
its own
A quick search method for audio signals based on a piecewise linear representation of feature trajectories
This paper presents a new method for a quick similarity-based search through
long unlabeled audio streams to detect and locate audio clips provided by
users. The method involves feature-dimension reduction based on a piecewise
linear representation of a sequential feature trajectory extracted from a long
audio stream. Two techniques enable us to obtain a piecewise linear
representation: the dynamic segmentation of feature trajectories and the
segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method
guarantees the same search results as the search method without the proposed
feature-dimension reduction method in principle. Experiment results indicate
significant improvements in search speed. For example the proposed method
reduced the total search time to approximately 1/12 that of previous methods
and detected queries in approximately 0.3 seconds from a 200-hour audio
database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and
Language Processin
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search
Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale
data processing and analytics, particularly for analyzing multimedia contents
which are often of high dimensionality. Instead of using exact NN search,
extensive research efforts have been focusing on approximate NN search
algorithms. In this work, we present "HDIdx", an efficient high-dimensional
indexing library for fast approximate NN search, which is open-source and
written in Python. It offers a family of state-of-the-art algorithms that
convert input high-dimensional vectors into compact binary codes, making them
very efficient and scalable for NN search with very low space complexity
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