{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantitative-analysis-of-automatic-image","title":"Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study","arxiv_id":"1701.01480","date":"2017-01-05","proceeding":null,"authors":["Yi-Ling Chen","Tzu-Wei Huang","Kai-Han Chang","Yu-Chen Tsai","Hwann-Tzong Chen","Bing-Yu Chen"],"abstract":"Automatic photo cropping is an important tool for improving visual quality of\ndigital photos without resorting to tedious manual selection. Traditionally,\nphoto cropping is accomplished by determining the best proposal window through\nvisual quality assessment or saliency detection. In essence, the performance of\nan image cropper highly depends on the ability to correctly rank a number of\nvisually similar proposal windows. Despite the ranking nature of automatic\nphoto cropping, little attention has been paid to learning-to-rank algorithms\nin tackling such a problem. In this work, we conduct an extensive study on\ntraditional approaches as well as ranking-based croppers trained on various\nimage features. In addition, a new dataset consisting of high quality cropping\nand pairwise ranking annotations is presented to evaluate the performance of\nvarious baselines. The experimental results on the new dataset provide useful\ninsights into the design of better photo cropping algorithms.","url_abs":"http://arxiv.org/abs/1701.01480v1","url_pdf":"http://arxiv.org/pdf/1701.01480v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quantitative-analysis-of-automatic-image","repo_url":"https://github.com/yiling-chen/flickr-cropping-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-cropping","task_name":"Image Cropping"},{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"}],"methods":[],"datasets_introduced":[{"slug":"flickr-cropping-dataset","name":"Flickr Cropping Dataset","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.01480","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}