Image sorting via a reduction in travelling salesman problem

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Image sorting via a reduction in travelling salesman problem

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© The Institution of Engineering and Technology
Received 17/07/2018, Accepted 18/09/2019, Revised 23/11/2018, Published 08/10/2019

The authors define and approximately solve the problem of unsupervised image sorting that is considered as a kind of content-based image clustering. The content-based image sorting is the creation of a route that passes through all the images once, in such an order that the next one from the previous image has similar content. In the end, an image ordering (e.g. slideshow) is automatically produced, so that the images with similar content should be close to each other. This problem resembles the problem known in the literature as ‘travelling salesman problem’ (TSP). In this work, the authors have proposed two classes of methods (the nearest-neighbour and genetic methods) that have also been applied on the TSP problem. Their benefits on computational efficiency and accuracy are discussed over six datasets that have been created from the GHIM-10K dataset. The experimental results demonstrate that the proposed methods efficiently solve the image sorting problem, producing image sequences that almost agree with human intuition.

Inspec keywords: travelling salesman problems; image retrieval; image sequences; content-based retrieval; pattern clustering; genetic algorithms; sorting

Other keywords: content-based image clustering; unsupervised image sorting; content-based image sorting; similar content; travelling salesman problem; TSP problem; image sequences; GHIM-10K dataset

Subjects: Optical, image and video signal processing; Computer vision and image processing techniques; Optimisation techniques; Information retrieval techniques; Optimisation techniques; Data handling techniques; Combinatorial mathematics

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