JN-Logo: A Logo Database for Aesthetic Visual Analysis

Abstract

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 are needed to provide data support for related work. Existing aesthetic visual analysis databases contain mainly photographs and works of art. There is no true logo database, and there are few public and high-quality design material databases. Facing these challenges, this paper introduces a larger-scale logo database named JN-Logo. JN-Logo provides 14,917 logo images from three well-known websites around the world and uses the votes of 150 graduate students. JN-Logo provides three types of annotation: aesthetic, style and semantic. JN-Logo’s scoring system includes 6 scoring points, 6 style labels and 11 semantic descriptions. Aesthetic annotations are divided into 0–5 points to evaluate the visual aesthetics of a logo image: the worst is 0 points; the best is 5 points. We demonstrate five advantages of the JN-Logo database: logo images as data objects, rich human annotations, quality scores for image aesthetics, style attribute labels and semantic description of style. We establish a baseline for JN-Logo to measure the effectiveness of its performance on algorithmic models of people’s choices of logo images. We compare existing traditional handcrafted and deep-learned features in both the aesthetic scoring task and the style-labeling task, showing the advantages of deep learning features. In the logo attribute classification task, the EfficientNet _B1 model achieved the best results, reaching an accuracy of 0.524. Finally, we describe two applications of JN-Logo: generating logo design style and similarity retrieval of logo content. The database of this article will eventually be made public.