869 research outputs found
Efficient storage and decoding of SURF feature points
Practical use of SURF feature points in large-scale indexing and retrieval engines requires an efficient means for storing and decoding these features. This paper investigates several methods for compression and storage of SURF feature points, considering both storage consumption and disk-read efficiency. We compare each scheme with a baseline plain-text encoding scheme as used by many existing SURF implementations. Our final proposed scheme significantly reduces both the time required to load and decode feature points, and the space required to store them on disk
The Many Qualities of a New Directly Accessible Compression Scheme
We present a new variable-length computation-friendly encoding scheme, named
SFDC (Succinct Format with Direct aCcesibility), that supports direct and fast
accessibility to any element of the compressed sequence and achieves
compression ratios often higher than those offered by other solutions in the
literature. The SFDC scheme provides a flexible and simple representation
geared towards either practical efficiency or compression ratios, as required.
For a text of length over an alphabet of size and a fixed
parameter , the access time of the proposed encoding is proportional
to the length of the character's code-word, plus an expected
overhead, where
is the -th number of the Fibonacci sequence. In the overall it uses
bits, where is the length of the encoded string.
Experimental results show that the performance of our scheme is, in some
respects, comparable with the performance of DACs and Wavelet Tees, which are
among of the most efficient schemes. In addition our scheme is configured as a
\emph{computation-friendly compression} scheme, as it counts several features
that make it very effective in text processing tasks. In the string matching
problem, that we take as a case study, we experimentally prove that the new
scheme enables results that are up to 29 times faster than standard
string-matching techniques on plain texts.Comment: 33 page
Compressed Text Indexes:From Theory to Practice!
A compressed full-text self-index represents a text in a compressed form and
still answers queries efficiently. This technology represents a breakthrough
over the text indexing techniques of the previous decade, whose indexes
required several times the size of the text. Although it is relatively new,
this technology has matured up to a point where theoretical research is giving
way to practical developments. Nonetheless this requires significant
programming skills, a deep engineering effort, and a strong algorithmic
background to dig into the research results. To date only isolated
implementations and focused comparisons of compressed indexes have been
reported, and they missed a common API, which prevented their re-use or
deployment within other applications.
The goal of this paper is to fill this gap. First, we present the existing
implementations of compressed indexes from a practitioner's point of view.
Second, we introduce the Pizza&Chili site, which offers tuned implementations
and a standardized API for the most successful compressed full-text
self-indexes, together with effective testbeds and scripts for their automatic
validation and test. Third, we show the results of our extensive experiments on
these codes with the aim of demonstrating the practical relevance of this novel
and exciting technology
Efficient Pattern Matching on Binary Strings
The binary string matching problem consists in finding all the occurrences of
a pattern in a text where both strings are built on a binary alphabet. This is
an interesting problem in computer science, since binary data are omnipresent
in telecom and computer network applications. Moreover the problem finds
applications also in the field of image processing and in pattern matching on
compressed texts. Recently it has been shown that adaptations of classical
exact string matching algorithms are not very efficient on binary data. In this
paper we present two efficient algorithms for the problem adapted to completely
avoid any reference to bits allowing to process pattern and text byte by byte.
Experimental results show that the new algorithms outperform existing solutions
in most cases.Comment: 12 page
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