70,198 research outputs found
Fast Exact Search in Hamming Space with Multi-Index Hashing
There is growing interest in representing image data and feature descriptors
using compact binary codes for fast near neighbor search. Although binary codes
are motivated by their use as direct indices (addresses) into a hash table,
codes longer than 32 bits are not being used as such, as it was thought to be
ineffective. We introduce a rigorous way to build multiple hash tables on
binary code substrings that enables exact k-nearest neighbor search in Hamming
space. The approach is storage efficient and straightforward to implement.
Theoretical analysis shows that the algorithm exhibits sub-linear run-time
behavior for uniformly distributed codes. Empirical results show dramatic
speedups over a linear scan baseline for datasets of up to one billion codes of
64, 128, or 256 bits
A Framework for Fast Classification Algorithms
Today, due to globalization of the world the size of data set is increasing, it is necessary to discover the
knowledge. The discovery of knowledge can be typically in the form of association rules, classification rules,
clustering, discovery of frequent episodes and deviation detection. Fast and accurate classifiers for large
databases are an important task in data mining. There is growing evidence that integrating classification and
association rules mining, classification approaches based on heuristic, greedy search like decision tree induction.
Emerging associative classification algorithms have shown good promises on producing accurate classifiers. In
this paper we focus on performance of associative classification and present a parallel model for classifier
building. For classifier building some parallel-distributed algorithms have been proposed for decision tree
induction but so far no such work has been reported for associative classification
A walk through the webās video clips
Approximately 10^5 video clips are posted every day on the Web. The popularity of Web-based video databases poses a number of challenges to machine vision scientists: how do we organize, index and search such large wealth of data? Content-based video search and classification have been proposed in the literature and applied successfully to analyzing movies, TV broadcasts and lab-made videos. We explore the performance of some of these algorithms on a large data-set of approximately 3000 videos. We collected our data-set directly from the Web minimizing bias for content or quality, way so as to have a faithful representation of the statistics of this medium. We find that the algorithms that we have come to trust do not work well on video clips, because their quality is lower and their subject is more varied. We will make the data publicly available to encourage further research
Fast Color Quantization Using Weighted Sort-Means Clustering
Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented. The method involves several modifications
to the conventional (batch) k-means algorithm including data reduction, sample
weighting, and the use of triangle inequality to speed up the nearest neighbor
search. Experiments on a diverse set of images demonstrate that, with the
proposed modifications, k-means becomes very competitive with state-of-the-art
color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table
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