Abstract
We present an algorithm for visually searching image collections using free-hand sketched queries. Prior sketch based image retrieval (SBIR) algorithms adopt either a category-level or fine-grain (instance-level) definition of cross-domain similarity—returning images that match the sketched object class (category-level SBIR), or a specific instance of that object (fine-grain SBIR). In this paper we take the middle-ground; proposing an SBIR algorithm that returns images sharing both the object category and key visual characteristics of the sketched query without assuming photo-approximate sketches from the user. We describe a deeply learned cross-domain embedding in which ‘mid-grain’ sketch-image similarity may be measured, reporting on the efficacy of unsupervised and semi-supervised manifold alignment techniques to encourage better intra-category (mid-grain) discrimination within that embedding. We propose a new mid-grain sketch-image dataset (MidGrain65c) and demonstrate not only mid-grain discrimination, but also improved category-level discrimination using our approach.
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Acknowledgments
This work was supported in part via an EPSRC doctoral training studentship (EP/M508160/1) and in part by UGPN/RCF 2017, FAPESP (grants 2016/16111-4, 2017/10068-2 and 2013/07375-0) and CNPq Fellowship (#307973/2017-4).
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Bui, T., Ribeiro, L., Ponti, M., Collomosse, J. (2019). Deep Manifold Alignment for Mid-Grain Sketch Based Image Retrieval. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_20
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