Aesthetic quality assessment for real estate images through deep learning methods

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2022-12-12

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Uçan, Nazlı Özge

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In this thesis, we aim to find the aesthetic quality of real estate images. Although aesthetic assessment is a subjective terminology, it is highly correlated with photographic rules. The aesthetic quality of images in real estate affects the decision of potential people of interest. The aesthetic evaluation of images is established via the Aesthetic Visual Assessment (AVA) dataset benchmark. Although AVA is a publicly available and diverse image dataset, it cannot be adapted to the real estate domain. Therefore, we constructed the Real-Estate Aesthetics Assessment Dataset (RAAD), which consists of real and synthetic real estate images. In order to gather subjective user data on the RAAD, a user study is conducted on a custom web-based scoring platform, serving RAAD image data. We analyzed several different methods involving classical vision classifiers and deep image classification models in order to assign an aesthetic quality score to the given real estate image. The results of those different approaches are presented comparatively on the RAAD data.

Subject Keywords

Aesthetic quality assessment, Real-estate image quality classification

URI

https://hdl.handle.net/11511/101871

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

N. Ö. Uçan, “Aesthetic quality assessment for real estate images through deep learning methods,” M.S. - Master of Science, Middle East Technical University, 2022.