# To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos

arXiv: https://arxiv.org/abs/1609.01388 · HTML: https://mlsanity.com/paper/1609.01388/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2016.

## Built on by (most cited first, 30 of 34)

- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2203.12745/) (2022)
- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://mlsanity.com/paper/2107.09609/) (2021)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [R2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding](https://mlsanity.com/paper/2404.00801/) (2024)
- [Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment](https://mlsanity.com/paper/2406.19255/) (2024)
- [Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding](https://mlsanity.com/paper/2311.08835/) (2023)
- [Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval](https://mlsanity.com/paper/2407.15051/) (2024)
- [Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling](https://mlsanity.com/paper/2308.05081/) (2023)
- [Spatial-Semantic Collaborative Cropping for User Generated Content](https://mlsanity.com/paper/2401.08086/) (2024)
- [Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding](https://mlsanity.com/paper/2403.14174/) (2024)
- [MS-DETR: Towards Effective Video Moment Retrieval and Highlight Detection by Joint Motion-Semantic Learning](https://mlsanity.com/paper/2507.12062/) (2025)
- [AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant](https://mlsanity.com/paper/2111.15050/) (2021)
- [LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2501.10787/) (2025)
- [MMSys'22 Grand Challenge on AI-based Video Production for Soccer](https://mlsanity.com/paper/2202.01031/) (2022)
- [LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos](https://mlsanity.com/paper/2210.05840/) (2022)
- [Video Summarization Overview](https://mlsanity.com/paper/2210.11707/) (2022)
- [Unleash the Potential of CLIP for Video Highlight Detection](https://mlsanity.com/paper/2404.01745/) (2024)
- [Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence](https://mlsanity.com/paper/2407.13933/) (2024)
- [Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines](https://mlsanity.com/paper/2310.13859/) (2023)
- [LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN](https://mlsanity.com/paper/2201.09049/) (2022)
- [TLDW: Extreme Multimodal Summarisation of News Videos](https://mlsanity.com/paper/2210.08481/) (2022)
- [Towards Fine-Grained Video Question Answering](https://mlsanity.com/paper/2503.06820/) (2025)
- [Test-Time Adaptation for Video Highlight Detection Using Meta-Auxiliary Learning and Cross-Modality Hallucinations](https://mlsanity.com/paper/2508.04924/) (2025)
- [Videogenic: Identifying Highlight Moments in Videos with Professional Photographs as a Prior](https://mlsanity.com/paper/2211.12493/) (2022)
- [TCR: Short Video Title Generation and Cover Selection with Attention Refinement](https://mlsanity.com/paper/2304.12561/) (2023)
- [LTC-GIF: Attracting More Clicks on Feature-length Sports Videos](https://mlsanity.com/paper/2201.09077/) (2022)

## Abstract (arXiv)

Thumbnails play such an important role in online videos. As the most representative snapshot, they capture the essence of a video and provide the first impression to the viewers; ultimately, a great thumbnail makes a video more attractive to click and watch. We present an automatic thumbnail selection system that exploits two important characteristics commonly associated with meaningful and attractive thumbnails: high relevance to video content and superior visual aesthetic quality. Our system selects attractive thumbnails by analyzing various visual quality and aesthetic metrics of video frames, and performs a clustering analysis to determine the relevance to video content, thus making the resulting thumbnails more representative of the video. On the task of predicting thumbnails chosen by professional video editors, we demonstrate the effectiveness of our system against six baseline methods, using a real-world dataset of 1,118 videos collected from Yahoo Screen. In addition, we study what makes a frame a good thumbnail by analyzing the statistical relationship between thumbnail frames and non-thumbnail frames in terms of various image quality features. Our study suggests that the selection of a good thumbnail is highly correlated with objective visual quality metrics, such as the frame texture and sharpness, implying the possibility of building an automatic thumbnail selection system based on visual aesthetics.