{"id":"https://openalex.org/W2041910200","doi":"https://doi.org/10.1145/2695664.2695951","title":"Compact and discriminative approach for encoding spatial-relationship of visual words","display_name":"Compact and discriminative approach for encoding spatial-relationship of visual words","publication_year":2015,"publication_date":"2015-04-13","ids":{"openalex":"https://openalex.org/W2041910200","doi":"https://doi.org/10.1145/2695664.2695951","mag":"2041910200"},"language":"en","primary_location":{"id":"doi:10.1145/2695664.2695951","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2695664.2695951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5029625931","display_name":"Glauco V. Pedrosa","orcid":"https://orcid.org/0000-0001-5573-6830"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Glauco V. Pedrosa","raw_affiliation_strings":["University of Sao Paulo (USP), S\u00e3o Carlos, SP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sao Paulo (USP), S\u00e3o Carlos, SP, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014783020","display_name":"Agma J. M. Traina","orcid":"https://orcid.org/0000-0003-4929-7258"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Agma J. M. Traina","raw_affiliation_strings":["University of Sao Paulo (USP), S\u00e3o Carlos, SP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sao Paulo (USP), S\u00e3o Carlos, SP, Brazil","institution_ids":["https://openalex.org/I17974374"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17974374"],"apc_list":null,"apc_paid":null,"fwci":0.4055,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67214055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"92","last_page":"95"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9976999759674072,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9840999841690063,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7842919230461121},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6726034879684448},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6536508798599243},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6530119180679321},{"id":"https://openalex.org/keywords/visual-word","display_name":"Visual Word","score":0.6360968351364136},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6205461621284485},{"id":"https://openalex.org/keywords/bag-of-words-model-in-computer-vision","display_name":"Bag-of-words model in computer vision","score":0.5808526873588562},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5567314624786377},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5550621747970581},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5525915622711182},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5297982096672058},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.45147836208343506},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.44089779257774353},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.41192150115966797},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4102728068828583},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30296334624290466},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2802099585533142}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7842919230461121},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6726034879684448},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6536508798599243},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6530119180679321},{"id":"https://openalex.org/C189391414","wikidata":"https://www.wikidata.org/wiki/Q7936579","display_name":"Visual Word","level":4,"score":0.6360968351364136},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6205461621284485},{"id":"https://openalex.org/C167611913","wikidata":"https://www.wikidata.org/wiki/Q6884747","display_name":"Bag-of-words model in computer vision","level":5,"score":0.5808526873588562},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5567314624786377},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5550621747970581},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5525915622711182},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5297982096672058},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.45147836208343506},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.44089779257774353},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.41192150115966797},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4102728068828583},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30296334624290466},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2802099585533142},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2695664.2695951","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2695664.2695951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:www.producao.usp.br:BDPI/49013","is_oa":false,"landing_page_url":"http://www.producao.usp.br/handle/BDPI/49013","pdf_url":null,"source":{"id":"https://openalex.org/S4306401373","display_name":"Scientific Electronic Library Online (S\u00e3o Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2982035970","host_organization_name":"Costa Rican Department of Social Security","host_organization_lineage":["https://openalex.org/I2982035970"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7599999904632568}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2125574651","https://openalex.org/W2126338813","https://openalex.org/W2126833203","https://openalex.org/W2141377631","https://openalex.org/W2151103935","https://openalex.org/W2162915993"],"related_works":["https://openalex.org/W2158102958","https://openalex.org/W2348780717","https://openalex.org/W2086564093","https://openalex.org/W1995462736","https://openalex.org/W2938717424","https://openalex.org/W2028757524","https://openalex.org/W2548286644","https://openalex.org/W2009049007","https://openalex.org/W2588241640","https://openalex.org/W2347783461"],"abstract_inverted_index":{"The":[0,79,117,179],"Bag-of-Visual-Words":[1],"approach":[2,38,190],"has":[3],"been":[4],"successfully":[5],"used":[6],"for":[7,57],"video":[8],"and":[9,19,184],"image":[10,85,182],"analysis":[11],"by":[12],"encoding":[13,44],"local":[14],"features":[15],"as":[16,93,160],"visual":[17,28,49,136,177],"words,":[18,137],"the":[20,27,32,48,84,101,104,115,126,132,139,161,165,192,202],"final":[21,118,127],"representation":[22,119],"is":[23,55,81],"a":[24,67],"histogram":[25],"of":[26,36,43,47,103,106,114,135,141,176],"words":[29,50],"detected":[30,91,108],"in":[31,181],"image.":[33],"One":[34],"limitation":[35],"this":[37,63],"relies":[39],"on":[40],"its":[41],"inability":[42],"spatial":[45,72,133,174],"distribution":[46],"within":[51],"an":[52],"image,":[53],"which":[54],"important":[56],"similarity":[58],"measurement":[59],"between":[60],"images.":[61],"In":[62],"paper,":[64],"we":[65,99],"present":[66],"novel":[68],"technique":[69],"to":[70,82,110,130,144,156,172,196],"incorporate":[71],"information,":[73],"called":[74],"Global":[75],"Spatial":[76,162],"Arrangement":[77],"(GSA).":[78],"idea":[80],"split":[83],"space":[86],"into":[87,125],"quadrants":[88],"using":[89,151],"each":[90,107,112],"point":[92,109],"origin.":[94],"To":[95],"ensure":[96],"rotation":[97],"invariance,":[98],"use":[100],"information":[102,124,171,175],"gradient":[105],"define":[111],"quarter":[113],"quadrant.":[116],"uses":[120],"only":[121],"two":[122],"extra":[123],"feature":[128],"vector":[129],"encode":[131,173],"arrangement":[134],"with":[138],"advantage":[140],"being":[142,201],"invariant":[143],"rotation.":[145],"We":[146],"performed":[147],"representative":[148],"experimental":[149],"evaluations":[150],"several":[152],"public":[153],"datasets.":[154],"Compared":[155],"other":[157,197],"techniques,":[158,199],"such":[159],"Pyramid":[163],"(SP),":[164],"proposed":[166,189],"method":[167],"needs":[168],"90%":[169],"less":[170],"words.":[178],"results":[180],"retrieval":[183,193],"classification":[185],"demonstrated":[186],"that":[187],"our":[188],"improved":[191],"accuracy":[194],"compared":[195],"traditional":[198],"while":[200],"most":[203],"compact":[204],"descriptor.":[205]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}