# Analyzing Images for Music Recommendation

arXiv: https://arxiv.org/abs/2105.07135 · HTML: https://mlsanity.com/paper/2105.07135/

## 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.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature](https://mlsanity.com/paper/1505.00855/) (2015)
- [Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark](https://mlsanity.com/paper/1605.02677/) (2016)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias](https://mlsanity.com/paper/1808.02212/) (2018)
- [Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks](https://mlsanity.com/paper/1705.07543/) (2017)
- [Learning Affective Correspondence between Music and Image](https://mlsanity.com/paper/1904.00150/) (2019)

## 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.