Evaluating image retrieval systems in a quantitative way, for example by computing measures like mean average precision, allows for objective comparisons with a ground-truth. However, in cases where ground-truth is not available, the only alternative is to collect feedback from a user. Thus, qualitative assessments become important to better understand how the system works. Visualizing the results could be, in some scenarios, the only way to evaluate the results obtained and also the only opportunity to identify that a system is failing. This necessitates developing a User Interface (UI) for a Content Based Image Retrieval (CBIR) system that allows visualization of results and improvement via capturing user relevance feedback. A well-designed UI facilitates understanding of the performance of the system, both in cases where it works well and perhaps more importantly those which highlight the need for improvement. Our open-source system implements three components to facilitate researchers to quickly develop these capabilities for their retrieval engine. We present: a web-based user interface to visualize retrieval results and collect user annotations; a server that simplifies connection with any underlying CBIR system; and a server that manages the search engine data.
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Publication Type = Conference or Workshop Item
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Subject = Computer Science: Image processing
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Subject = Computer Science: Information storage and retrieval systems
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Status = Published
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Subject = Computer Science: Information retrieval
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
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DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
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Dublin City University ->
Subject = Computer Science: Machine learning
Noel E. O'Connor,
Xavier Giro-i-Nieto,
Kevin McGuinness,
Eva Mohedano,
Paula Gomez Duran