CLEAR: A Fully User-side Image Search System
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Abstract (arXiv)
We use many search engines on the Internet in our daily lives. However, they are not perfect. Their scoring function may not model our intent or they may accept only text queries even though we want to carry out a similar image search. In such cases, we need to make a compromise: We continue to use the unsatisfactory service or leave the service. Recently, a new solution, user-side search systems, has been proposed. In this framework, each user builds their own search system that meets their preference with a user-defined scoring function and user-defined interface. Although the concept is appealing, it is still not clear if this approach is feasible in practice. In this demonstration, we show the first fully user-side image search system, CLEAR, which realizes a similar-image search engine for Flickr. The challenge is that Flickr does not provide an official similar image search engine or corresponding API. Nevertheless, CLEAR realizes it fully on a user-side. CLEAR does not use a backend server at all nor store any images or build search indices. It is in contrast to traditional search algorithms that require preparing a backend server and building a search index. Therefore, each user can easily deploy their own CLEAR engine, and the resulting service is custom-made and privacy-preserving. The online demo is available at https://clear.joisino.net. The source code is available at https://github.com/joisino/clear.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2022.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
6 papers.
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- 2023Making Translators Privacy-aware on the User's Sidecited by 3
- 2024Overhead-free User-side Recommender Systemscited by 2
- 2025Solving the Cold Start Problem on One's Own as an End User via Preference Transfercited by 1
- 2022Active Learning from the Webcited by 0
- 2026AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Sidecited by 0
- 2026AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side25 Sep 2026
- 2025Solving the Cold Start Problem on One's Own as an End User via Preference Transfer18 Feb 2025
- 2024Overhead-free User-side Recommender Systems12 Nov 2024
- 2023Making Translators Privacy-aware on the User's Side7 Dec 2023
- 2023Embarrassingly Simple Text Watermarks13 Oct 2023
- 2022Active Learning from the Web15 Oct 2022