{"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/a-location-aware-embedding-technique-for","title":"A location-aware embedding technique for accurate landmark recognition","arxiv_id":"1704.05754","date":"2017-04-19","proceeding":null,"authors":["Federico Magliani","Navid Mahmoudian Bidgoli","Andrea Prati"],"abstract":"The current state of the research in landmark recognition highlights the good\naccuracy which can be achieved by embedding techniques, such as Fisher vector\nand VLAD. All these techniques do not exploit spatial information, i.e.\nconsider all the features and the corresponding descriptors without embedding\ntheir location in the image. This paper presents a new variant of the\nwell-known VLAD (Vector of Locally Aggregated Descriptors) embedding technique\nwhich accounts, at a certain degree, for the location of features. The driving\nmotivation comes from the observation that, usually, the most interesting part\nof an image (e.g., the landmark to be recognized) is almost at the center of\nthe image, while the features at the borders are irrelevant features which do\nno depend on the landmark. The proposed variant, called locVLAD (location-aware\nVLAD), computes the mean of the two global descriptors: the VLAD executed on\nthe entire original image, and the one computed on a cropped image which\nremoves a certain percentage of the image borders. This simple variant shows an\naccuracy greater than the existing state-of-the-art approach. Experiments are\nconducted on two public datasets (ZuBuD and Holidays) which are used both for\ntraining and testing. Morever a more balanced version of ZuBuD is proposed.","url_abs":"http://arxiv.org/abs/1704.05754v1","url_pdf":"http://arxiv.org/pdf/1704.05754v1.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":[],"tasks":[{"task_slug":"landmark-recognition","task_name":"Landmark Recognition"}],"methods":[],"datasets_introduced":[{"slug":"zubud","name":"ZuBuD+","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}