{"id":310025,"date":"2016-10-23T22:47:02","date_gmt":"2016-10-24T05:47:02","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=310025"},"modified":"2018-10-16T20:01:28","modified_gmt":"2018-10-17T03:01:28","slug":"monet-system-reliving-memories-theme-based-photo-storytelling","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/monet-system-reliving-memories-theme-based-photo-storytelling\/","title":{"rendered":"Monet: A System for Reliving Your Memories by Theme-based Photo Storytelling"},"content":{"rendered":"

With the ever-increasing use of smartphones and digital cameras, people are now able to take photos anywhere and anytime. Most of these photos simply end up stored in the cloud without further interaction. This occurs because we lack intelligent services to organize these personal photos well. Therefore, there is an urgent need for such a system to enable people to relive their memories by turning their photos into stories. This paper presents a storytelling system named Monet, which automatically creates interesting stories from personal photos by mimicking cinematic knowledge based on a set of predesigned editing styles. The system consists of two stages: photo summarization, which selects a subset of the \u201cbest\u201d photos to represent a photo collection, and story remixing, which generates a stylish music video from the selected photos. During photo summarization, photos are grouped into events based on multimodal features (time and location). The \u201cbest\u201d photos are then selected according to visual quality, event representativeness, and diversity. The second stage, story remixing, automatically selects an appropriate theme-dependent editing style based on the photo content. Each selected photo is converted to a video clip by applying a virtual camera with appropriate motions. A series of video effects, color filters, shapes, and transitions are then applied to the video clips according to cinematic rules. The generated video is finally multiplexed with a music clip to generate the story. Evaluations show that our system achieves superior performance to state-of-the-art photo event detection and story generation systems.<\/p>\n","protected":false},"excerpt":{"rendered":"

With the ever-increasing use of smartphones and digital cameras, people are now able to take photos anywhere and anytime. Most of these photos simply end up stored in the cloud without further interaction. This occurs because we lack intelligent services to organize these personal photos well. Therefore, there is an urgent need for such a […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562,13551],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-310025","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_publishername":"","msr_edition":"IEEE Trans on Multimedia","msr_affiliation":"","msr_published_date":"2016-10-21","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"IEEE Trans on Multimedia","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":"310028","msr_publicationurl":"http:\/\/dx.doi.org\/10.1109\/TMM.2016.2614185","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"monet-camera","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/10\/Monet-Camera.pdf","id":310028,"label_id":0},{"type":"url","title":"http:\/\/dx.doi.org\/10.1109\/TMM.2016.2614185","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/dx.doi.org\/10.1109\/TMM.2016.2614185"}],"msr-author-ordering":[{"type":"text","value":"Yue Wu","user_id":0,"rest_url":false},{"type":"text","value":"Xu Shen","user_id":0,"rest_url":false},{"type":"user_nicename","value":"tmei","user_id":34188,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=tmei"},{"type":"text","value":"Xinmei Tian","user_id":0,"rest_url":false},{"type":"text","value":"Nenghai Yu","user_id":0,"rest_url":false},{"type":"text","value":"Yong Rui","user_id":0,"rest_url":false},{"type":"user_nicename","value":"yongrui","user_id":35040,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=yongrui"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[144916],"msr_project":[239357,212095],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":239357,"post_title":"Video Analysis","post_name":"video-analytics","post_type":"msr-project","post_date":"2016-06-16 19:35:23","post_modified":"2017-10-07 21:38:55","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/video-analytics\/","post_excerpt":"Video has become ubiquitous on the Internet, broadcasting channels, as well as that captured by personal devices. This has encouraged the development of advanced techniques to analyze the semantic video content for a wide variety of applications, such as video representation learning [CVPR 2017], video highlight detection [CVPR 2016], video summarization, object detection, action recognition [CVPR 2016, ICMR 2016], semantic segmentation, and so on. Highlight detection The emergence of wearable devices such as portable cameras…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/239357"}]}},{"ID":212095,"post_title":"Photo Story","post_name":"photo-story","post_type":"msr-project","post_date":"2016-01-25 19:33:26","post_modified":"2017-10-07 21:38:02","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/photo-story\/","post_excerpt":"The capability of managing personal photos is becoming crucial. In this work, we have attempted to solve the following pain points for mobile users: 1) intelligent photo tagging, best photo selection, event segmentation and album naming, 2) speech recognition and user intent parsing of time, location, people attributes and objects, 3) search by arbitrary queries. We first segment and categorize the unstructured photo streams into multiple semantic-related albums in an automatic way. 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