Abstract
<p>With the explosion of data on the internet led to the presence of the big data era, so it requires data processing in order to get the useful information. One of the challenges is the gesture recognition the video processing. Therefore, this study proposes Latent-Dynamic Conditional Neural Fields and compares with the other family members of Conditional Random Fields. To improve the accuracy, these methods are combined by using Fuzzy Clustering. From the result, it can be concluded that the performance of Latent-Dynamic Conditional Neural Fields are lower than Conditional Neural Fields but higher than the Conditional Random Fields and Latent-Dynamic Conditional Random Fields. Also, the combination of Latent-Dynamic Conditional Neural Fields and Fuzzy C-Means Clustering has the highest. This evaluation is tested in a temporal dataset of gesture phase segmentation.</p>
Highlights
THE development of the Internet is rapidly increasing since 1990
The analysis was conducted by comparing among the basic classifiers and the combination with their fuzzy filtering
The sensitivity is reduced by half. It means that the increase is not necessary because it makes the performance of FCRF be decreased
Summary
THE development of the Internet is rapidly increasing since 1990. It has led to an explosion of data many times with the presence of the social media era. Data with all kinds of formats from the text, audio, and video either structured or not has been uploaded on the internet. Even very large sized data is very fast growing exponentially every second. Data with the condition often is known as big data. It is a trend which attracted much attention of researchers to study it. One of the challenges in big data is processing of sequential data. One of the interesting tasks in the process is labeling
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