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Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems
Ahmad, J. Sajjad, M. Mehmood, Irfan Rho, S. Baik, S.W.
Ahmad, J.
Sajjad, M.
Mehmood, Irfan
Rho, S.
Baik, S.W.
Publication Date
2017-09
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© Springer-Verlag Berlin Heidelberg 2015. Reproduced in accordance with the publisher's self-archiving policy.
The final publication is available at Springer via https://doi.org/10.1007/s11554-015-0536-0.
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Yes
Open Access status
openAccess
Accepted for publication
2015-09-29
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Abstract
The exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.
Version
Accepted manuscript
Citation
Ahmad J, Sajjad M, Mehmood I et al (2017) Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. Journal of Real-Time Image Processing. 13(3): 431-447.
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Article