Analyzing Images for Music Recommendation
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Abstract (arXiv)
Experiencing images with suitable music can greatly enrich the overall user experience. The proposed image analysis method treats an artwork image differently from a photograph image. Automatic image classification is performed using deep-learning based models. An illustrative analysis showcasing the ability of our deep-models to inherently learn and utilize perceptually relevant features when classifying artworks is also presented. The Mean Opinion Score (MOS) obtained from subjective assessments of the respective image and recommended music pairs supports the effectiveness of our approach.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 15 May 2021.
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9 of 32 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
- 2015Cyclical Learning Rates for Training Neural Networkscited by 629
- 2015Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Featurecited by 182
- 2016Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmarkcited by 71
- 2013Recognizing Image Stylecited by 64
- 2018Contemplating Visual Emotions: Understanding and Overcoming Dataset Biascited by 41
- 2017Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networkscited by 14
- 2019Learning Affective Correspondence between Music and Imagecited by 8
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