{"id":157144,"date":"2009-06-01T00:00:00","date_gmt":"2009-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/picking-the-best-daisy\/"},"modified":"2018-10-16T21:39:36","modified_gmt":"2018-10-17T04:39:36","slug":"picking-the-best-daisy","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/picking-the-best-daisy\/","title":{"rendered":"Picking the Best Daisy"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Local image descriptors that are highly discriminative, computational efficient, and with low storage footprint have long been a dream goal of computer vision research. In this paper, we focus on learning such descriptors, which make use of the DAISY configuration and are simple to compute both sparsely and densely. We develop a new training set of match\/non-match image patches which improves on previous work. We test a wide variety of gradient and steerable filter based configurations and optimize over all parameters to obtain low matching errors for the descriptors. We further explore robust normalization, dimension reduction and dynamic range reduction to increase the discriminative power and yet reduce the storage requirement of the learned descriptors. All these enable us to obtain highly efficient local descriptors: e.g, 13:2% error at 13 bytes storage per descriptor, compared with 26:1% error at 128 bytes for SIFT.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Local image descriptors that are highly discriminative, computational efficient, and with low storage footprint have long been a dream goal of computer vision research. In this paper, we focus on learning such descriptors, which make use of the DAISY configuration and are simple to compute both sparsely and densely. We develop a new training set [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"IEEE Computer Society","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Computer Vision and Pattern Recognition","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 IEEE. Reprinted from IEEE Computer Society.This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.By choosing to view this document, you agree to all provisions of the copyright laws protecting it.","msr_conference_name":"Computer Vision and Pattern Recognition","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2009-06-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"http:\/\/www.cs.ubc.ca\/~mbrown\/patchdata\/patchdata.html","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2009,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-157144","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"IEEE Computer Society","msr_edition":"Computer Vision and Pattern Recognition","msr_affiliation":"","msr_published_date":"2009-06-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"223966","msr_publicationurl":"http:\/\/www.cs.ubc.ca\/~mbrown\/patchdata\/patchdata.html","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"winder_hua_brown_cvpr09.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/06\/winder_hua_brown_cvpr09.pdf","id":223966,"label_id":0},{"type":"url","title":"http:\/\/www.cs.ubc.ca\/~mbrown\/patchdata\/patchdata.html","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":0,"url":"http:\/\/www.cs.ubc.ca\/~mbrown\/patchdata\/patchdata.html"},{"id":223966,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/06\/winder_hua_brown_cvpr09.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"swinder","user_id":33778,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=swinder"},{"type":"text","value":"Gang Hua","user_id":0,"rest_url":false},{"type":"text","value":"Matthew Brown","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[430839,170255],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":430839,"post_title":"On-device ML for Object and Activity Detection","post_name":"device-ml-ambient-aware-applications","post_type":"msr-project","post_date":"2017-10-05 11:03:40","post_modified":"2020-03-13 17:08:00","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/device-ml-ambient-aware-applications\/","post_excerpt":"To process data locally, we have accelerated ML computations via ASICs that incorporate efficient pipelining and parallelism techniques. 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