# Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

## TL;DR

This paper introduces a training-free approach for image aesthetics assessment that leverages composite features from pretrained deep models, outperforming existing methods without requiring model fine-tuning.

## Contribution

The method utilizes off-the-shelf deep features from global, local, and scene-aware information, demonstrating superior performance over state-of-the-art techniques.

## Key findings

- Deep residual networks produce more aesthetics-aware features.
- Composite features improve overall assessment accuracy.
- The approach outperforms existing methods on benchmark datasets.

## Abstract

Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common large-scale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.