{"id":"https://openalex.org/W3187822442","doi":"https://doi.org/10.1145/3461598.3461605","title":"Hand-Crafted and Learned Spatiotemporal Filters to Inform and Track Visual Saliency","display_name":"Hand-Crafted and Learned Spatiotemporal Filters to Inform and Track Visual Saliency","publication_year":2021,"publication_date":"2021-04-10","ids":{"openalex":"https://openalex.org/W3187822442","doi":"https://doi.org/10.1145/3461598.3461605","mag":"3187822442"},"language":"en","primary_location":{"id":"doi:10.1145/3461598.3461605","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3461598.3461605","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics &amp; Swarm Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019958503","display_name":"Khaled Aboumerhi","orcid":"https://orcid.org/0000-0002-4980-3598"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Khaled Aboumerhi","raw_affiliation_strings":["The Johns Hopkins University, USA"],"affiliations":[{"raw_affiliation_string":"The Johns Hopkins University, USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037936459","display_name":"Ralph Etienne\u2010Cummings","orcid":"https://orcid.org/0000-0003-4445-973X"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ralph Etienne-Cummings","raw_affiliation_strings":["The Johns Hopkins University, USA"],"affiliations":[{"raw_affiliation_string":"The Johns Hopkins University, USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079092811","display_name":"Jonah Sengputa","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonah Sengputa","raw_affiliation_strings":["The Johns Hopkins University, USA"],"affiliations":[{"raw_affiliation_string":"The Johns Hopkins University, USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035582292","display_name":"John Rattray","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Rattray","raw_affiliation_strings":["The Johns Hopkins University, USA"],"affiliations":[{"raw_affiliation_string":"The Johns Hopkins University, USA","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5019958503"],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10272822,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2014","issue":null,"first_page":"44","last_page":"48"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7835595011711121},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.7332030534744263},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6638326048851013},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.6304620504379272},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5594677329063416},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5428206920623779},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4839763641357422},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4829201400279999},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.470680296421051},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.44243842363357544},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.43700867891311646},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4319410026073456},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.4186314642429352},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3557611405849457},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3305944800376892}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7835595011711121},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.7332030534744263},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6638326048851013},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.6304620504379272},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5594677329063416},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5428206920623779},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4839763641357422},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4829201400279999},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.470680296421051},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.44243842363357544},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.43700867891311646},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4319410026073456},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.4186314642429352},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3557611405849457},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3305944800376892},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3461598.3461605","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3461598.3461605","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics &amp; Swarm Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.6399999856948853,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1980711281","https://openalex.org/W2039735372","https://openalex.org/W2104062382","https://openalex.org/W2132051469","https://openalex.org/W2162152641","https://openalex.org/W2469278928","https://openalex.org/W2626498001","https://openalex.org/W2798859500","https://openalex.org/W3003820562","https://openalex.org/W4226051885"],"related_works":["https://openalex.org/W1578117154","https://openalex.org/W2542256560","https://openalex.org/W1543936162","https://openalex.org/W1971776229","https://openalex.org/W2766961550","https://openalex.org/W2112263445","https://openalex.org/W1497101000","https://openalex.org/W2054235656","https://openalex.org/W1504288058","https://openalex.org/W2030712947"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"an":[3,8],"event-tracking":[4],"algorithm":[5],"based":[6],"on":[7,150],"unsupervised":[9],"learning":[10,17,88],"method":[11,95],"to":[12,52,122,163],"follow":[13],"salient":[14,32],"features.":[15],"By":[16],"spatiotemporal":[18,43,110],"filters":[19,60,68,89,111],"using":[20],"computationally":[21],"inexpensive":[22],"distance":[23],"metrics":[24],"such":[25],"as":[26,42,153,155],"determinant":[27],"comparisons,":[28],"we":[29,46,127,146],"show":[30,108],"that":[31,78,87],"features":[33,99],"are":[34,61,69,112],"captured":[35],"by":[36,142],"the":[37,135,156],"learned":[38,113],"activation":[39],"prototypes,":[40],"known":[41],"templates.":[44],"First,":[45],"discuss":[47],"previous":[48],"hand-crafted":[49,67],"filter":[50],"methods":[51],"capture":[53],"spike-based":[54,161],"data.":[55],"While":[56],"spatial":[57],"and":[58,72],"temporal":[59],"easily":[62],"crafted":[63],"for":[64,75,158],"obvious":[65],"features,":[66],"not":[70,80],"robust":[71],"exhaustive":[73],"templates":[74],"detecting":[76],"events":[77],"may":[79],"be":[81],"so":[82],"obvious.":[83],"It":[84],"becomes":[85],"clear":[86],"is":[90],"a":[91,115,129,138,159],"more":[92],"diverse,":[93],"rectifying":[94],"in":[96,134],"identifying":[97],"important":[98],"while":[100],"remaining":[101],"independent":[102],"from":[103],"human":[104],"observations.":[105],"We":[106],"then":[107],"how":[109],"through":[114],"series":[116,130],"of":[117,131,137],"prototype":[118],"clustering.":[119],"In":[120],"order":[121],"handle":[123],"information":[124],"over":[125],"time,":[126],"propose":[128],"decision":[132],"trees":[133],"form":[136],"random":[139],"forest":[140],"inspired":[141],"lifelong":[143],"learning.":[144],"Finally,":[145],"conclude":[147],"promising":[148],"results":[149],"feature":[151],"tracking,":[152],"well":[154],"need":[157],"ground-truth":[160],"data-set":[162],"validate":[164],"saliency":[165],"algorithms.":[166]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
