ViXeN: View eXtract aNnotate Multimedia Data

Post provided by KadaMbari Devarajan

At a time when data is everywhere, and data science is being talked about as the future in different fields, a method that produces huge amounts of multimedia data is camera-trapping. We need ways to manage these kinds of media data efficiently. ViXeN is an attempt to do just that.

Camera traps have been a game-changer for ecological studies, especially those involving mammals in the wild. This has resulted in an increasing amount of camera trap datasets. However, the tools to manage camera trap data tend to be very specific and customised for images. They typically come with stringent data organisation requirements. There’s a growing amount of multimedia datasets and a lack of tools that can manage several types of media data.

In ‘ViXeN: An open‐source package for managing multimedia data’ we try to fix this visible gap. Camera trap management is a very specific a use-case. We thought that the field was missing general-purpose tools, capable of handling a variety of media data and formats, that were also free and open source. ViXeN was born from this idea. It stands for View eXtract aNnotate (media data). The name is also an ode to the canids I was studying at the time which included two species of foxes.


Example camera trap video

Finding a Better Way: The Origins of ViXeN

The idea for ViXeN came during the data analysis phase of my MSc thesis. I had several thousand videos to go through based on the camera-trapping study I had just finished. There seemed to be plenty of tools for managing camera trap data.

When I tried to enter my data though, I realised that there were no tools available to efficiently manage video data. After spending some time manually entering the information into spreadsheets based on watching the videos, I was frustrated. I thought there must be a better, faster, less error-prone way to do this. After discussing this with Prabhu Ramachandran (my partner in life, an aerospace engineer who moonlights as a software developer), we decided to collaborate and address this problem.

ViXeN used for a project involving a camera trap video

ViXeN used for a project involving a camera trap video.

Flexible Data Management Software

ViXeN is a general-purpose software package to manage different kinds of multimedia data (text, audio, video, images, and PDFs) in a variety of formats. It’s free and open source, and compatible with Linux, Windows, and MacOS. We strongly believe this is quite unique in that it is applicable across fields and domains, and highly customizable at the same time. It has been used to manage camera trap data (both images as well videos), bird call data (audio files), and even literature reviews (using PDFs). Even better, projects involving multiple types of media data can be managed as well. The software isn’t specific to just ecological studies, it can be used in any field that involves these kinds of media data.

For instance, I’ve used ViXeN to manage papers as part of a literature review, in a way that’s different from current reference management software (but it can be used alongside these). With ViXeN, you can enter data and extract metadata faster, and cataloguing and archiving are much easier too. It’s capable of handling large datasets in the era of big data, and data can be downloaded as a comma separated value (csv) file for subsequent analysis. Existing data can be imported as well. It’s easy to use and offers the flexibility of adding user-defined tags.

ViXeN used for PDFs as part of a literature review

ViXeN used for PDFs as part of a literature review.

We hope that ViXeN will help researchers in different fields to manage their projects involving multimedia datasets, and that you’ll consider using ViXeN for your next research project.

To find out more about ViXeN, read our article ‘ViXeN: An open‐source package for managing multimedia data’. This article is freely available, no subscription required.

This article was shortlisted for the Robert May Prize 2018. You can find all of the shortlisted articles is this Virtual Issue.

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