On the Extraction and Classification of Hand Outlines

Davis, Luke M.; Theobald, Barry-John; Toms, Andoni P.; Bagnall, Anthony

doi:10.1007/978-3-642-23878-9_12

Cited by 10 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The images were converted to 1-d series by initially calculating the outlines of the 1045 hand x-ray images using the algorithm described in [4]. The locations of the tips and webs of the hand were extracted from these outlines using the algorithm presented in [3].…”

Section: New Data Setsmentioning

confidence: 99%

A shapelet transform for time series classification

Lines

Davis

Hills

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

290

233

View full text Add to dashboard Cite

The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet transformation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transformation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets.

show abstract

Section: New Data Setsmentioning

confidence: 99%

A shapelet transform for time series classification

Lines

Davis

Hills

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

290

233

View full text Add to dashboard Cite

show abstract

“…We constructed a training set of 1000 images and a test set of 370 images, taken from [21]. Each image was automatically segmented with an algorithm described in [8] and manually labelled as correct or incorrect by three human subjects. The outlines were then mapped onto a one-dimensional series based on the Euclidean distance of each pixel along the contour to the first point, and these series were smoothed using a median filter and z-normalised (across the series) to remove the scaling effect of age variation (see Figure 3).…”

Section: Wcci 2008 Competition: Ford a And Bmentioning

confidence: 99%

Transformation Based Ensembles for Time Series Classification

Bagnall¹,

Davis²,

Hills³

et al. 2012

Proceedings of the 2012 SIAM International Conference on Data Mining

Self Cite

View full text Add to dashboard Cite

Until recently, the vast majority of data mining time series classification (TSC) research has focused on alternative distance measures for 1-Nearest Neighbour (1-NN) classifiers based on either the raw data, or on compressions or smoothing of the raw data. Despite the extensive evidence in favour of 1-NN classifiers with Euclidean or Dynamic Time Warping distance, there has also been a flurry of recent research publications proposing classification algorithms for TSC. Generally, these classifiers describe different ways of incorporating summary measures in the time domain into more complex classifiers. Our hypothesis is that the easiest way to gain improvement on TSC problems is simply to transform into an alternative data space where the discriminatory features are more easily detected. To test our hypothesis, we perform a range of benchmarking experiments in the time domain, before evaluating nearest neighbour classifiers on data transformed into the power spectrum, the autocorrelation function, and the principal component space. We demonstrate that on some problems there is dramatic improvement in the accuracy of classifiers built on the transformed data over classifiers built in the time domain, but that there is also a wide variance in accuracy for a particular classifier built on different data transforms. To overcome this variability, we propose a simple transformation based ensemble, then demonstrate that it improves performance and reduces the variability of classifiers built in the time domain only. Our advice to a practitioner with a real world TSC problem is to try transforms before developing a complex classifier; it is the easiest way to get a potentially large increase in accuracy, and may provide further insights into the underlying relationships that characterise the problem.

show abstract

“…roughly centered, currently done manually. Recent work by Davis et al [7] provides encouraging results on automating this process.…”

Section: Related Workmentioning

confidence: 99%

A CRF approach to fitting a generalized hand skeleton model

Mihail

Blomquist

Jacobs

2014

IEEE Winter Conference on Applications of Computer Vision

View full text Add to dashboard Cite

We present a new point distribution model capable of modeling joint subluxation (shifting) in rheumatoid arthritis (RA) patients and an approach to fitting this model to posteroanterior view hand radiographs. We formulate this shape fitting problem as inference in a conditional random field. This model combines potential functions that focus on specific anatomical structures and a learned shape prior. We evaluate our approach on two datasets: one containing relatively healthy hands and one containing hands of rheumatoid arthritis patients. We provide an empirical analysis of the relative value of different potential functions. We also show how to use the fitted hand skeleton to initialize a process for automatically estimating bone contours, which is a challenging, but important, problem in RA disease progression assessment.

show abstract

On the Extraction and Classification of Hand Outlines

Cited by 10 publications

References 8 publications

A shapelet transform for time series classification

A shapelet transform for time series classification

Transformation Based Ensembles for Time Series Classification

A CRF approach to fitting a generalized hand skeleton model

Contact Info

Product

Resources

About