A Compression Based Distance Measure for Texture

Campana, Bilson; Keogh, Eamonn

doi:10.1137/1.9781611972801.74

Cited by 24 publications

(52 citation statements)

References 15 publications

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“…Unlike other methods of the literature that extract explicit features directly related to the image texture, the distance measure applied by RPCD is based on the Kolmogorov complexity to compare the texture similarity between two images. To do this, CK-1 exploits the resultant compression of a synthetic video created from the two images to be compared [3].…”

Section: Related Workmentioning

confidence: 99%

“…We show in our experimental evaluation with 38 time series data sets that TFRP is very competitive with state-of-the-art methods such as 1-NN with Euclidean distance, Dynamic Time Warping and Recurrence Patterns Compression Distance (RPCD) [2]. RPCD is our previous attempt to classify time series using recurrence plots and CK-1 [3], a distance measure between images that uses video compression algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Texture Features for Time Series Classification

Souza

Silva

Batista

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-Time series are present in many pattern recognition applications related to medicine, biology, astronomy, economy, and others. In particular, the classification task has attracted much attention from a large number of researchers. In such a task, empirical researches has shown that the 1-Nearest Neighbor rule with a distance measure in time domain usually performs well in a variety of application domains. However, certain time series features are not evident in time domain. A classical example is the classification of sound, in which representative features are usually present in the frequency domain. For these applications, an alternative representation is necessary. In this work we investigate the use of recurrence plots as data representation for time series classification. This representation has well-defined visual texture patterns and their graphical nature exposes hidden patterns and structural changes in data. Therefore, we propose a method capable of extracting texture features from this graphical representation, and use those features to classify time series data. We use traditional methods such as Grey Level Co-occurrence Matrix and Local Binary Patterns, which have shown good results in texture classification. In a comprehensible experimental evaluation, we show that our method outperforms the state-ofthe-art methods for time series classification.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extracting Texture Features for Time Series Classification

Souza

Silva

Batista

2014

2014 22nd International Conference on Pattern Recognition

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“…The distance between a subset of S, comprised of S i,n and another subsequence S j,n is the CK distance [8], denoted dist(S i,n , S j,n ).…”

Section: Notationmentioning

confidence: 99%

“…The CK distance measure is a relatively new, compression-based similarity measure, which exploits MPEG video encoding to measure the similarity between real-valued images [8]. The distance between two equalsized images (denoted as x and y) is calculated as: In Section IV, we will explain and justify the choice of this particular distance function [8].…”

Section: Notationmentioning

confidence: 99%

“…Image processing algorithms themselves are not generally devoid of the need for feature extraction. However, we propose to use the CK distance measure [8], a recently introduced compressionbased measure that avoids explicitly extracting any features, thus remains parameter-free. We will show that the CK distance measure is so efficient that even a brute-force implementation can run in about real-time for a typical song.…”

Section: Introductionmentioning

confidence: 99%

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Parameter-Free Audio Motif Discovery in Large Data Archives

Hao

Shokoohi-Yekta

Papageorgiou³

et al. 2013

2013 IEEE 13th International Conference on Data Mining

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Abstract-The discovery of repeated structure, i.e. motifs/nearduplicates, is often the first step in exploratory data mining. As such, the last decade has seen extensive research efforts in motif discovery algorithms for text, DNA, time series, protein sequences, graphs, images, and video. Surprisingly, there has been less attention devoted to finding repeated patterns in audio sequences, in spite of their ubiquity in science and entertainment. While there is significant work for the special case of motifs in music, virtually all this work makes many assumptions about data (often to the point of being genre specific) and thus these algorithms do not generalize to audio sequences containing animal vocalizations, industrial processes, or a host of other domains that we may wish to explore.In this work we introduce a novel technique for finding audio motifs. Our method does not require any domainspecific tuning and is essentially parameter-free. We demonstrate our algorithm on very diverse domains, finding audio motifs in laboratory mice vocalizations, wild animal sounds, music, and human speech. Our experiments demonstrate that our ideas are effective in discovering objectively correct or subjectively plausible motifs. Moreover, we show our novel probabilistic early abandoning approach is efficient, being two to three orders of magnitude faster than brute-force search, and thus faster than real-time for most problems.

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