JN-Logo: A Logo Database for Aesthetic Visual Analysis
نویسندگان
چکیده
Data are an important part of machine learning. In recent years, it has become increasingly common for researchers to study artificial intelligence-aided design, and rich design materials needed provide data support related work. Existing aesthetic visual analysis databases contain mainly photographs works art. There is no true logo database, there few public high-quality material databases. Facing these challenges, this paper introduces a larger-scale database named JN-Logo. JN-Logo provides 14,917 images from three well-known websites around the world uses votes 150 graduate students. types annotation: aesthetic, style semantic. JN-Logo’s scoring system includes 6 points, labels 11 semantic descriptions. Aesthetic annotations divided into 0–5 points evaluate aesthetics image: worst 0 points; best 5 points. We demonstrate five advantages database: as objects, human annotations, quality scores image aesthetics, attribute description style. establish baseline measure effectiveness its performance on algorithmic models people’s choices images. compare existing traditional handcrafted deep-learned features in both task style-labeling task, showing deep learning features. classification EfficientNet _B1 model achieved results, reaching accuracy 0.524. Finally, we describe two applications JN-Logo: generating similarity retrieval content. The article will eventually be made public.
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ژورنال
عنوان ژورنال: Electronics
سال: 2022
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics11193248