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Markup SVG—An Online Content-Aware Image Abstraction and Annotation Tool

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3 Author(s)
Edward Kim ; Dept. of Comput. Sci. & Eng., Lehigh Univ., Bethlehem, PA, USA ; Xiaolei Huang ; Gang Tan

Suppose you want to effectively search through millions of images, train an algorithm to perform image and video object recognition, or research the complex patterns and relationships that exist in our visual world. A common and essential component for any of these tasks is a large annotated image dataset. However, obtaining labeled image data is a complex and tedious task that requires methods for annotating and structuring content. Therefore, we developed a comprehensive online tool and data structure, Markup SVG, that simplifies the collection of annotated image data by leveraging state-of-the-art image processing techniques. As the core data structure of our tool, we adopt scalable vector graphics (SVG), an extensible and versatile language built upon XML. Given the extensibility of our framework, we are able to encode low-level image features, high-level semantics, and further define interactions with the data to assist the user with image annotation. We also demonstrate the ability to merge multiple online and offline datasets into our system in an effort to standardize image collection and its data representation. Lastly, we present our modular design; each component acts as a plug-in to our system. We developed several novel components and algorithms to highlight the possibilities of semi-supervised segmentation and automatic annotation within our proposed framework. Further, our modular design provides the necessary capabilities to incorporate future image features, methods, or algorithms. Our results show that our tool is able to greatly simplify the process of obtaining large annotated image collections in an online collaborative platform.

Published in:

IEEE Transactions on Multimedia  (Volume:13 ,  Issue: 5 )