Memorability based image to image translation

Sathisha Basavaraju; Prasen Kumar Sharma; Arijit Sur

doi:10.1117/12.2556543

31 January 2020 Memorability based image to image translation

Sathisha Basavaraju, Prasen Kumar Sharma, Arijit Sur

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331G (2020) https://doi.org/10.1117/12.2556543
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

This paper presents a memorability based image-to-image translation technique to make an image more memorable while retaining its high-level contents. Conventionally, the image-to-image translation task aims to learn the mapping between images of two different domains using a set of aligned image pairs. However, dataset having such one-to-one mapping is not available for memorability based image-to-image translation. Therefore, the aim of the proposed task is defined to learn the mapping F: I → I' between two image domains I and I'. Here, I corresponds to input image domain and I' is the unknown image domain containing the modified version of the input images. Also, every image in I' is more memorable than its corresponding image in I. Therefore, the proposed task is achieved by developing a deep learning based method to learn the mapping F: I→ I' using mean-squared error and memorability loss between I and F(I). The experimental results showed that the proposed approach increases the memorability of the given image better than the state-of-the-art image-to-image translation techniques.

Citation Download Citation

Sathisha Basavaraju, Prasen Kumar Sharma, and Arijit Sur "Memorability based image to image translation", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331G (31 January 2020); https://doi.org/10.1117/12.2556543

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