Temporal segmentation of video objects for hierarchical object-based motion description
This paper describes a hierarchical approach for object-based motion description of video in terms of object motions and object-to-object interactions. We present a temporal hierarchy for object motion description, which consists of low-level elementary motion units (EMU) and high-level action units...
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| Veröffentlicht in: | IEEE transactions on image processing 2002-02, Vol.11 (2), p.135-145 |
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| container_title | IEEE transactions on image processing |
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| creator | Fu, Y. Ekin, A. Tekalp, A.M. Mehrotra, R. |
| description | This paper describes a hierarchical approach for object-based motion description of video in terms of object motions and object-to-object interactions. We present a temporal hierarchy for object motion description, which consists of low-level elementary motion units (EMU) and high-level action units (AU). Likewise, object-to-object interactions are decomposed into a hierarchy of low-level elementary reaction units (ERU) and high-level interaction units (IU). We then propose an algorithm for temporal segmentation of video objects into EMUs, whose dominant motion can be described by a single representative parametric model. The algorithm also computes a representative (dominant) affine model for each EMU. We also provide algorithms for identification of ERUs and for classification of the type of ERUs. Experimental results demonstrate that segmenting the life-span of video objects into EMUS and ERUs facilitates the generation of high-level visual summaries for fast browsing and navigation. At present, the formation of high-level action and interaction units is done interactively. We also provide a set of query-by-example results for low-level EMU retrieval from a database based on similarity of the representative dominant affine models. |
| doi_str_mv | 10.1109/83.982821 |
| format | Article |
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We present a temporal hierarchy for object motion description, which consists of low-level elementary motion units (EMU) and high-level action units (AU). Likewise, object-to-object interactions are decomposed into a hierarchy of low-level elementary reaction units (ERU) and high-level interaction units (IU). We then propose an algorithm for temporal segmentation of video objects into EMUs, whose dominant motion can be described by a single representative parametric model. The algorithm also computes a representative (dominant) affine model for each EMU. We also provide algorithms for identification of ERUs and for classification of the type of ERUs. Experimental results demonstrate that segmenting the life-span of video objects into EMUS and ERUs facilitates the generation of high-level visual summaries for fast browsing and navigation. At present, the formation of high-level action and interaction units is done interactively. We also provide a set of query-by-example results for low-level EMU retrieval from a database based on similarity of the representative dominant affine models.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/83.982821</identifier><identifier>PMID: 18244619</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Extravehicular activity ; Gold ; Hierarchies ; Humans ; Image processing ; Indexing ; Information retrieval ; Information, signal and communications theory ; Layout ; Mathematical models ; Multimedia databases ; Navigation ; Object motion ; Parametric statistics ; Pattern recognition. Digital image processing. Computational geometry ; Segmentation ; Signal processing ; Studies ; Telecommunications and information theory ; Temporal logic ; Visual databases</subject><ispartof>IEEE transactions on image processing, 2002-02, Vol.11 (2), p.135-145</ispartof><rights>2002 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2002</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-6bda90aacc12afcf4b792d5e6978c3ac3f2f63ed1f8a203c68cdf9be9dbd3e903</citedby><cites>FETCH-LOGICAL-c453t-6bda90aacc12afcf4b792d5e6978c3ac3f2f63ed1f8a203c68cdf9be9dbd3e903</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/982821$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/982821$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=13466882$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18244619$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fu, Y.</creatorcontrib><creatorcontrib>Ekin, A.</creatorcontrib><creatorcontrib>Tekalp, A.M.</creatorcontrib><creatorcontrib>Mehrotra, R.</creatorcontrib><title>Temporal segmentation of video objects for hierarchical object-based motion description</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>This paper describes a hierarchical approach for object-based motion description of video in terms of object motions and object-to-object interactions. We present a temporal hierarchy for object motion description, which consists of low-level elementary motion units (EMU) and high-level action units (AU). Likewise, object-to-object interactions are decomposed into a hierarchy of low-level elementary reaction units (ERU) and high-level interaction units (IU). We then propose an algorithm for temporal segmentation of video objects into EMUs, whose dominant motion can be described by a single representative parametric model. The algorithm also computes a representative (dominant) affine model for each EMU. We also provide algorithms for identification of ERUs and for classification of the type of ERUs. Experimental results demonstrate that segmenting the life-span of video objects into EMUS and ERUs facilitates the generation of high-level visual summaries for fast browsing and navigation. At present, the formation of high-level action and interaction units is done interactively. We also provide a set of query-by-example results for low-level EMU retrieval from a database based on similarity of the representative dominant affine models.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Extravehicular activity</subject><subject>Gold</subject><subject>Hierarchies</subject><subject>Humans</subject><subject>Image processing</subject><subject>Indexing</subject><subject>Information retrieval</subject><subject>Information, signal and communications theory</subject><subject>Layout</subject><subject>Mathematical models</subject><subject>Multimedia databases</subject><subject>Navigation</subject><subject>Object motion</subject><subject>Parametric statistics</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Segmentation</subject><subject>Signal processing</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><subject>Temporal logic</subject><subject>Visual databases</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0c9rFTEQB_Agiq3Vg1cPshT8ddiaSbL5cZSitlDwUvG4ZJOJzePt5pnsK_jfm-cuFnqopwzMJzMwX0JeAj0DoOaj5mdGM83gETkGI6ClVLDHtaadahUIc0SelbKhFEQH8ik5As2EkGCOyY9rHHcp221T8OeI02znmKYmheY2ekxNGjbo5tKElJubiNlmdxNd5UujHWxB34zp7y-PxeW4O9TPyZNgtwVfrO8J-f7l8_X5RXv17evl-aer1omOz60cvDXUWueA2eCCGJRhvkNplHbcOh5YkBw9BG0Z5U5q54MZ0PjBczSUn5B3y9xdTr_2WOZ-jMXhdmsnTPvSK847bhSXVb59UDKtKDVC_x8q1RmhDvD9gxCkAq4pZ1Dp6T26Sfs81cv0WguuoVOsog8LcjmVkjH0uxxHm3_3QPtD0L3m_RJ0ta_XgfthRH8n12QreLMCW2peIdvJxXLnuJBS68PSV4uLiPivvW75AwSSuEw</recordid><startdate>20020201</startdate><enddate>20020201</enddate><creator>Fu, Y.</creator><creator>Ekin, A.</creator><creator>Tekalp, A.M.</creator><creator>Mehrotra, R.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Digital image processing. Computational geometry</topic><topic>Segmentation</topic><topic>Signal processing</topic><topic>Studies</topic><topic>Telecommunications and information theory</topic><topic>Temporal logic</topic><topic>Visual databases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fu, Y.</creatorcontrib><creatorcontrib>Ekin, A.</creatorcontrib><creatorcontrib>Tekalp, A.M.</creatorcontrib><creatorcontrib>Mehrotra, R.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fu, Y.</au><au>Ekin, A.</au><au>Tekalp, A.M.</au><au>Mehrotra, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Temporal segmentation of video objects for hierarchical object-based motion description</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2002-02-01</date><risdate>2002</risdate><volume>11</volume><issue>2</issue><spage>135</spage><epage>145</epage><pages>135-145</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>This paper describes a hierarchical approach for object-based motion description of video in terms of object motions and object-to-object interactions. We present a temporal hierarchy for object motion description, which consists of low-level elementary motion units (EMU) and high-level action units (AU). Likewise, object-to-object interactions are decomposed into a hierarchy of low-level elementary reaction units (ERU) and high-level interaction units (IU). We then propose an algorithm for temporal segmentation of video objects into EMUs, whose dominant motion can be described by a single representative parametric model. The algorithm also computes a representative (dominant) affine model for each EMU. We also provide algorithms for identification of ERUs and for classification of the type of ERUs. Experimental results demonstrate that segmenting the life-span of video objects into EMUS and ERUs facilitates the generation of high-level visual summaries for fast browsing and navigation. At present, the formation of high-level action and interaction units is done interactively. We also provide a set of query-by-example results for low-level EMU retrieval from a database based on similarity of the representative dominant affine models.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>18244619</pmid><doi>10.1109/83.982821</doi><tpages>11</tpages></addata></record> |
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| subjects | Algorithms Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Extravehicular activity Gold Hierarchies Humans Image processing Indexing Information retrieval Information, signal and communications theory Layout Mathematical models Multimedia databases Navigation Object motion Parametric statistics Pattern recognition. Digital image processing. Computational geometry Segmentation Signal processing Studies Telecommunications and information theory Temporal logic Visual databases |
| title | Temporal segmentation of video objects for hierarchical object-based motion description |
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