Atlas AI is pioneering in the image data mining front by developing an interactive, real-time Image Data Science toolkit that empowers the user with SQL-like query functionality directly in the image space.

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The IDS toolkit uses advanced Graph-theoretic constructs coupled with state-of-the-art Machine Learning algorithms for user-driven image segmentation. Each segment is delivered with a pool of features that describe it in structural, textural and radiometric terms. 

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These features can be in turn, sourced into existing data-science frameworks for further analytics that offer unique insights into the image information content.

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IDS was developed to address segmentation challenges in the absence of training data and suitable or suitably-trained models. It can be operated in three different modes:

• Supervised mode: driven by limited-size collections of training data or by prescribed parametrization,
• Semi-supervised mode: driven by limited-size collections of  binary object templates,
• Unsupervised mode: no prior knowledge on targeted image features. 

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IDS can be used to deliver real-time segmentation output and insights or generate new training data-sets by avoiding the time and cost intensive manual annotation.

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The images below demonstrate the use of IDS in construction site monitoring under the unsupervised segmentation schema, i.e. in the complete absence of training data.

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