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Updated: Feb 8, 2026
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
10.0K
Deep Active Learning with Contaminated Tags for Image Aesthetics Assessment.
Summary
A novel semi-supervised deep active learning (SDAL) algorithm accurately assesses image aesthetics by learning human gaze shifting paths (GSP). This method effectively identifies important image regions and incorporates multiple users' aesthetic experiences.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Image aesthetic quality assessment is crucial for various applications but faces challenges in semantic description, human perception modeling, and multi-user experience integration.
- Conventional methods struggle with noisy tags, accurately reflecting human visual attention, and incorporating diverse aesthetic preferences.
Purpose of the Study:
- To develop a novel semi-supervised deep active learning (SDAL) algorithm for improved image aesthetic quality assessment.
- To address limitations of existing methods by effectively discovering semantically important image regions and simulating human perception.
- To integrate multiple users' aesthetic experiences into a robust assessment model.
Main Methods:
- Proposed a semi-supervised deep active learning (SDAL) algorithm utilizing BING object patches to simulate human visual perception.
- Developed a hierarchical learning approach for gaze shifting paths (GSP), unifying region discovery and feature learning within a principled framework.
- Employed sparsity penalty to discard noisy features and a probabilistic model for aesthetic assessment, incorporating professional photographers' experiences.
Main Results:
- The SDAL algorithm demonstrated superiority in image aesthetic quality assessment across benchmark datasets.
- Generated Gaze Shifting Paths (GSP) showed high consistency (93%) with real human eye-tracking data.
- The probabilistic model effectively encoded multiple users' aesthetic experiences and integrated auxiliary quality features.
Conclusions:
- The proposed SDAL algorithm offers a significant advancement in image aesthetic quality assessment by mimicking human perception and learning.
- The method's ability to handle noisy data and integrate diverse aesthetic experiences makes it highly effective.
- The high correlation between learned GSPs and human gaze paths validates the model's approach to simulating visual attention.
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