7,730 research outputs found
On the Dynamic Time Warping of Cyclic Sequences for Shape Retrieval
In the last years, in shape retrieval, methods based on Dynamic Time Warping and sequences where each point of the contour is represented by elements of several dimensions have had a significant presence. In this approach each point of the closed contour contains information with respect to the other ones, this global information is very discriminant. The current state-of-the-art shape retrieval is based on the analysis of these distances to learn better ones.
These methods are robust to noise and invariant to transformations, but, they obtain the invariance to the starting point with a brute force cyclic alignment which has a high computational time. In this work, we present the Cyclic Dynamic Time Warping. It can obtain the cyclic alignment in O(n2 log n) time, where n is the size of both sequences. Experimental results show that our proposal is a better alternative than the brute force cyclic alignment and other heuristics for obtaining this invariance
Feature extraction for speech and music discrimination
Driven by the demand of information retrieval, video editing and human-computer interface, in this paper we propose a novel spectral feature for music and speech discrimination. This scheme attempts to simulate a biological model using the averaged cepstrum, where human perception tends to pick up the areas of large cepstral changes. The cepstrum data that is away from the mean value will be exponentially reduced in magnitude. We conduct experiments of music/speech discrimination by comparing the performance of the proposed feature with that of previously proposed features in classification. The dynamic time warping based classification verifies that the proposed feature has the best quality of music/speech classification in the test database
Classification of functional brain data for multimedia retrieval
This study introduces new signal processing methods for extracting meaningful information from brain signals (functional magnetic resonance imaging and single unit recording) and proposes a content-based retrieval system for functional brain data. First, a new method that combines maximal overlapped discrete wavelet transforms (MODWT) and dynamic time warping (DTW) is presented as a solution for dynamically detecting the hemodynamic response from fMRI data. Second, a new method for neuron spike sorting is presented that uses the maximal overlap discrete wavelet transform and rotated principal component analysis. Third, a procedure to characterize firing patterns of neuron spikes from the human brain, in both the temporal domain and the frequency domain, is presented. The combination of multitaper spectral estimation and a polynomial curve-fitting method is employed to transform the firing patterns to the frequency domain. To generate temporal shapes, eight local maxima are smoothly connected by a cubic spline interpolation. A rotated principal component analysis is used to extract common firing patterns as templates from a training set of 4100 neuron spike signals. Dynamic time warping is then used to assign each neuron firing to the closest template without shift error. These techniques are utilized in the development of a content-based retrieval system for human brain data
Feature Trajectory Dynamic Time Warping for Clustering of Speech Segments
Dynamic time warping (DTW) can be used to compute the similarity between two
sequences of generally differing length. We propose a modification to DTW that
performs individual and independent pairwise alignment of feature trajectories.
The modified technique, termed feature trajectory dynamic time warping (FTDTW),
is applied as a similarity measure in the agglomerative hierarchical clustering
of speech segments. Experiments using MFCC and PLP parametrisations extracted
from TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent and
statistically significant improvements in the quality of the resulting clusters
in terms of F-measure and normalised mutual information (NMI).Comment: 10 page
Twofold Video Hashing with Automatic Synchronization
Video hashing finds a wide array of applications in content authentication,
robust retrieval and anti-piracy search. While much of the existing research
has focused on extracting robust and secure content descriptors, a significant
open challenge still remains: Most existing video hashing methods are fallible
to temporal desynchronization. That is, when the query video results by
deleting or inserting some frames from the reference video, most existing
methods assume the positions of the deleted (or inserted) frames are either
perfectly known or reliably estimated. This assumption may be okay under
typical transcoding and frame-rate changes but is highly inappropriate in
adversarial scenarios such as anti-piracy video search. For example, an illegal
uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion
etc by performing a cleverly designed non-uniform resampling of the video. We
present a new solution based on dynamic time warping (DTW), which can implement
automatic synchronization and can be used together with existing video hashing
methods. The second contribution of this paper is to propose a new robust
feature extraction method called flow hashing (FH), based on frame averaging
and optical flow descriptors. Finally, a fusion mechanism called distance
boosting is proposed to combine the information extracted by DTW and FH.
Experiments on real video collections show that such a hash extraction and
comparison enables unprecedented robustness under both spatial and temporal
attacks.Comment: submitted to Image Processing (ICIP), 2014 21st IEEE International
Conference o
Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201
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