{"id":"https://openalex.org/W1913341271","doi":"https://doi.org/10.1109/cvpr.2015.7298923","title":"Transformation-Invariant Convolutional Jungles","display_name":"Transformation-Invariant Convolutional Jungles","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1913341271","doi":"https://doi.org/10.1109/cvpr.2015.7298923","mag":"1913341271"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298923","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5049295603","display_name":"Dmitry Laptev","orcid":null},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Dmitry Laptev","raw_affiliation_strings":["ETH Zurich, Switzerland","Eth Z\u00fcrich, Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]},{"raw_affiliation_string":"Eth Z\u00fcrich, Switzerland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038199211","display_name":"Joachim M. Buhmann","orcid":"https://orcid.org/0000-0002-6613-7101"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Joachim M. Buhmann","raw_affiliation_strings":["ETH Zurich, Switzerland","Eth Z\u00fcrich, Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]},{"raw_affiliation_string":"Eth Z\u00fcrich, Switzerland","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049295603"],"corresponding_institution_ids":["https://openalex.org/I35440088"],"apc_list":null,"apc_paid":null,"fwci":2.8057,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.93646336,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3043","last_page":"3051"},"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9991000294685364,"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/T10057","display_name":"Face and Expression Recognition","score":0.9980999827384949,"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":0.7479158043861389},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6689300537109375},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6288909316062927},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.6254794597625732},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6097329258918762},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5699407458305359},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5180873274803162},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49709442257881165},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4959023892879486},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4720122516155243},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4400116801261902},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41184088587760925},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4116972088813782},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23978716135025024}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7479158043861389},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6689300537109375},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6288909316062927},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.6254794597625732},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6097329258918762},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5699407458305359},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5180873274803162},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49709442257881165},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4959023892879486},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4720122516155243},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4400116801261902},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41184088587760925},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4116972088813782},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23978716135025024},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","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":1,"locations":[{"id":"doi:10.1109/cvpr.2015.7298923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298923","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W39908396","https://openalex.org/W148877464","https://openalex.org/W1515620500","https://openalex.org/W1538131130","https://openalex.org/W1558448861","https://openalex.org/W1969013163","https://openalex.org/W1972715665","https://openalex.org/W1980038761","https://openalex.org/W2040679279","https://openalex.org/W2051669046","https://openalex.org/W2054981189","https://openalex.org/W2068777106","https://openalex.org/W2098458263","https://openalex.org/W2112796928","https://openalex.org/W2121647436","https://openalex.org/W2124386111","https://openalex.org/W2140262144","https://openalex.org/W2141125852","https://openalex.org/W2144578488","https://openalex.org/W2148809531","https://openalex.org/W2149194912","https://openalex.org/W2149197198","https://openalex.org/W2149706766","https://openalex.org/W2156919418","https://openalex.org/W2157629193","https://openalex.org/W2167510172","https://openalex.org/W2167999447","https://openalex.org/W2169596663","https://openalex.org/W2504108613","https://openalex.org/W2951539267","https://openalex.org/W4236137412","https://openalex.org/W4236796448","https://openalex.org/W6601634175","https://openalex.org/W6632100814","https://openalex.org/W6674568808","https://openalex.org/W6681636949","https://openalex.org/W6684372118"],"related_works":["https://openalex.org/W1670332068","https://openalex.org/W2095618524","https://openalex.org/W2735770592","https://openalex.org/W1971024059","https://openalex.org/W1502062143","https://openalex.org/W4224236531","https://openalex.org/W4291993329","https://openalex.org/W2063177452","https://openalex.org/W2413982977","https://openalex.org/W2268850994"],"abstract_inverted_index":{"Many":[0],"Computer":[1],"Vision":[2],"problems":[3],"arise":[4],"from":[5,63],"information":[6,62],"processing":[7],"of":[8,48,79,85,100,132],"data":[9],"sources":[10],"with":[11],"nuisance":[12],"variances":[13,34],"like":[14],"scale,":[15,86],"orientation,":[16],"contrast,":[17],"perspective":[18],"foreshortening":[19],"or":[20],"-":[21,25],"in":[22,122],"medical":[23,126],"imaging":[24],"staining":[26],"and":[27,40,105,125],"local":[28],"warping.":[29],"In":[30],"most":[31],"cases":[32],"these":[33,64],"can":[35,41,74],"be":[36,42],"stated":[37],"a":[38,53,76,101],"priori":[39],"used":[43],"to":[44,66],"improve":[45],"the":[46,97,117,133],"generalization":[47],"recognition":[49,124],"algorithms.":[50],"We":[51,115],"propose":[52],"novel":[54,102],"supervised":[55],"feature":[56],"learning":[57],"approach,":[58],"which":[59,108],"efficiently":[60],"extracts":[61],"constraints":[65],"produce":[67],"interpretable,":[68],"transformation-invariant":[69],"features.":[70],"The":[71],"proposed":[72],"method":[73],"incorporate":[75],"large":[77],"class":[78],"transformations,":[80,92],"e.g.,":[81],"shifts,":[82],"rotations,":[83],"change":[84],"morphological":[87],"operations,":[88],"non-linear":[89],"distortions,":[90],"photometric":[91],"etc.":[93],"These":[94],"features":[95],"boost":[96],"discrimination":[98],"power":[99],"image":[103],"classification":[104],"segmentation":[106],"method,":[107],"we":[109],"call":[110],"Transformation-Invariant":[111],"Convolutional":[112],"Jungles":[113],"(TICJ).":[114],"test":[116],"algorithm":[118],"on":[119],"two":[120],"benchmarks":[121],"face":[123],"imaging,":[127],"where":[128],"it":[129],"achieves":[130],"state":[131],"art":[134],"results,":[135],"while":[136],"being":[137],"computationally":[138],"significantly":[139],"more":[140],"efficient":[141],"than":[142],"Deep":[143],"Neural":[144],"Networks.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":4}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
