{"id":"https://openalex.org/W2535166515","doi":"https://doi.org/10.1109/dicta.2014.7008095","title":"An Evaluation of Sparseness as a Criterion for Selecting Independent Component Filters, When Applied to Texture Retrieval","display_name":"An Evaluation of Sparseness as a Criterion for Selecting Independent Component Filters, When Applied to Texture Retrieval","publication_year":2014,"publication_date":"2014-11-01","ids":{"openalex":"https://openalex.org/W2535166515","doi":"https://doi.org/10.1109/dicta.2014.7008095","mag":"2535166515"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2014.7008095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2014.7008095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","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/A5062072064","display_name":"Nabeel Mohammed","orcid":"https://orcid.org/0000-0002-7661-3570"},"institutions":[{"id":"https://openalex.org/I4210131323","display_name":"University of Asia Pacific","ror":"https://ror.org/03dk4hf38","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210131323"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Nabeel Mohammed","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I4210131323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032303916","display_name":"David Squire","orcid":"https://orcid.org/0000-0001-6738-8271"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"David McG. Squire","raw_affiliation_strings":["Clayton School of Information Technology, Monash University, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clayton School of Information Technology, Monash University, Victoria, Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26040511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9923999905586243,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9799000024795532,"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/redundancy","display_name":"Redundancy (engineering)","score":0.7153491973876953},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.6392112374305725},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6096787452697754},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6076322197914124},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5922847986221313},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5608378648757935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5461339950561523},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5399705171585083},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.46447455883026123},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33630698919296265},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1616848111152649}],"concepts":[{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7153491973876953},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.6392112374305725},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6096787452697754},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6076322197914124},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5922847986221313},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5608378648757935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5461339950561523},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5399705171585083},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.46447455883026123},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33630698919296265},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1616848111152649},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta.2014.7008095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2014.7008095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W111429590","https://openalex.org/W119990253","https://openalex.org/W1504388412","https://openalex.org/W1528058524","https://openalex.org/W1548802052","https://openalex.org/W1600428950","https://openalex.org/W1779098396","https://openalex.org/W1965728067","https://openalex.org/W1996101765","https://openalex.org/W2019502123","https://openalex.org/W2048361967","https://openalex.org/W2066155494","https://openalex.org/W2075843426","https://openalex.org/W2090334843","https://openalex.org/W2105464873","https://openalex.org/W2110519503","https://openalex.org/W2117731089","https://openalex.org/W2120274331","https://openalex.org/W2129142957","https://openalex.org/W2137234026","https://openalex.org/W2142260675","https://openalex.org/W2146672645","https://openalex.org/W2146796802","https://openalex.org/W2151677089","https://openalex.org/W2154594384","https://openalex.org/W2156187071","https://openalex.org/W2164149616","https://openalex.org/W2165307655","https://openalex.org/W2185411374","https://openalex.org/W2397066555","https://openalex.org/W2902424647","https://openalex.org/W2912889105","https://openalex.org/W3149314866","https://openalex.org/W4205778870","https://openalex.org/W4240035482","https://openalex.org/W6604458971","https://openalex.org/W6630249604","https://openalex.org/W6635618033","https://openalex.org/W6638042023","https://openalex.org/W6677802204","https://openalex.org/W6686518194","https://openalex.org/W6712620824","https://openalex.org/W6757019266"],"related_works":["https://openalex.org/W2357256365","https://openalex.org/W2348502264","https://openalex.org/W4298130764","https://openalex.org/W2365486383","https://openalex.org/W2362059367","https://openalex.org/W2804364458","https://openalex.org/W2494338568","https://openalex.org/W2350084742","https://openalex.org/W1495042958","https://openalex.org/W2901443725"],"abstract_inverted_index":{"In":[0,44,94],"this":[1,45,139],"paper":[2,46],"we":[3,47,96,153],"evaluate":[4],"the":[5,52,59,63,74,84,111,119,129,172],"utility":[6],"of":[7,16,54],"sparseness":[8,22,130],"as":[9],"a":[10,14,28,144,164],"criterion":[11],"for":[12,41],"selecting":[13],"sub-set":[15],"independent":[17],"component":[18],"filters":[19,86,90,101,113,122],"(ICF).":[20],"Four":[21],"measures":[23,131],"were":[24],"presented":[25],"more":[26],"than":[27],"decade":[29],"ago":[30],"by":[31,68,79],"Le":[32,69],"Borgne":[33,70],"et":[34,71],"al.,":[35,72],"but":[36],"have":[37,104],"since":[38],"been":[39],"ignored":[40],"ICF":[42,156],"selection.":[43],"present":[48],"our":[49,159],"evaluation":[50],"in":[51],"context":[53],"texture":[55,126],"retrieval.":[56,127],"We":[57,81,107],"compare":[58],"sparseness-based":[60],"method":[61,76,140],"with":[62,167],"dispersal-based":[64],"method,":[65,161],"also":[66,108],"proposed":[67,78],"and":[73,87,152],"clustering-based":[75,160],"previously":[77],"us.":[80],"show":[82,97,109,154],"that":[83,98,110,155],"sparse":[85,112,173],"highly":[88,99,120],"dispersed":[89,100,121],"are":[91,132],"quite":[92],"different.":[93],"fact":[95],"tend":[102],"to":[103,118,125,142],"lower":[105,169],"sparseness.":[106],"give":[114],"better":[115],"results":[116],"compared":[117],"when":[123],"applied":[124],"However":[128],"calculated":[133],"over":[134],"filter":[135,146,165],"response":[136],"energies,":[137],"making":[138],"susceptible":[141],"choosing":[143],"redundant":[145],"set.":[147],"This":[148],"issue":[149],"is":[150],"demonstrated":[151],"selected":[157],"using":[158],"which":[162],"chooses":[163],"set":[166],"much":[168],"redundancy,":[170],"outperforms":[171],"filters.":[174]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
