Evaluating attributed personality traits from scene perception probability

Zhu, Hancheng; Zhao, Sicheng; Jiang, Hongyan

doi:10.1016/j.patrec.2018.09.027

Cited by 17 publications

(2 citation statements)

References 23 publications

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“…Besides the aesthetic score, each image was annotated by several users with seven objective attributes: composition, light, color, depth of field, object emphasis, content, and scene category. For each user, the database also provided some subjective attributes: age, gender, education experience, artistic experience, photographic experience, and Big-Five personality traits [41]. The education experience was divided into six steps: junior high school, senior high school, technical secondary school, junior college, and university.…”

Section: Databasesmentioning

confidence: 99%

“…Although the two images rated by User #2 have the same average aesthetics, the user's personalized aesthetic scores for these two images differ greatly. This is because neurotic people prefer images with dim light and monotonous color [41], which led User #2 to give a higher aesthetic score to the left image than the right image. From the above analysis, we can draw a conclusion that the multiple objective attributes of images and the multiple subjective attributes of users jointly affect users' personalized aesthetic experiences for images, and the proposed multi-attribute interaction can effectively reveal the potential impact relationship between them.…”

Section: Visual Analysismentioning

confidence: 99%

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Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning

et al. 2022

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Due to the subjective nature of people’s aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignores the mutual influence of the multiple attributes of images and users in their personalized aesthetic experiences. To this end, this paper proposes a personalized image aesthetics assessment method via multi-attribute interactive reasoning. Different from existing PIAA models, the multi-attribute interaction constructed from both images and users is used as more effective prior knowledge. First, we designed a generic aesthetics extraction module from the perspective of images to obtain the aesthetic score distribution and multiple objective attributes of images rated by most users. Then, we propose a multi-attribute interactive reasoning network from the perspective of users. By interacting multiple subjective attributes of users with multiple objective attributes of images, we fused the obtained multi-attribute interactive features and aesthetic score distribution to predict personalized aesthetic scores. Experimental results on multiple PIAA datasets demonstrated our method outperformed state-of-the-art PIAA methods.

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Section: Databasesmentioning

confidence: 99%

Section: Visual Analysismentioning

confidence: 99%