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World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.


Fusion of dominant colour and spatial layout features for effective image retrieval of coloured logos and trademarks

Yan, Yijun and Ren, Jinchang and Li, Yinsheng and Windmill, James and Ijomah, Winifred (2015) Fusion of dominant colour and spatial layout features for effective image retrieval of coloured logos and trademarks. In: 2015 IEEE International Conference on Multimedia Big Data. IEEE, 306 - 311. ISBN 978-1-4799-8688-0

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Yan_etal_IEEE_BigMM_2015_Fusion_of_dominant_colour_and_spatial_layout_features_for_effective_image_retrieval.pdf - Accepted Author Manuscript

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Due to its uniqueness and high value in commercial side, logos and trademarks play a key role in e-business based global marketing. Detecting misused and faked logos need designated and accurate image processing and retrieval techniques. However, existing colour and shape based retrieval techniques, which are mainly designed for natural images, cannot provide effective retrieval of logo images. In this paper, an effective approach is proposed for content-based image retrieval of coloured logos and trademarks. By extracting the dominant colour from colour quantization and measuring the spatial similarity, fusion of colour and spatial layout features is achieved. The proposed approach has been tested on a database containing over 250 logo images. Experimental results show that the proposed methodology yields more accurate results in retrieving relevant images than conventional approaches even with added Gaussian and Salt&pepper noise.