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Image Hub Explorer: Journal Paper

August 26th, 2014

We were notified today that the extended version of the paper that we have presented at last year’s European Conference on Machine Learning has been accepted for publications in the Multimedia Tools and Applications journal. The paper is titled “Image Hub Explorer: Evaluating Representations and Metrics for Content-based Image Retrieval and Object Recognition“. The full text of the article will soon be available online on the publications page.

The paper is about the Image Hub Explorer system for interactive evaluation and visualization of the utility of various image feature representations and metrics from the perspective of the semantic consistency of the top-k result sets and the emergence of beneficial and/or detrimental image hubs in the data. Indeed, our results indicate that different image feature representations have different levels of susceptibility to the hubness phenomenon and the curse of dimensionality. In the paper, we have examined the quantized bag-of-feature representations for SIFT, SURF, ORB and BRIEF descriptors, though the system itself was build to be applicable to generic representations as well, including DeCaf and similar learned feature types.

The system implements state-of-the-art hubness-aware machine learning methods for metric learning, ranking and classification, as well as several novel visualization layers and components. It will be made freely available in about a month as a part of the Hub Miner library that is to be released soon as open source. We will post more notifications soon.

A video of the demo of Image Hub Explorer is available here.

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