Facial expression recognition via weighted group sparsity View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-04

AUTHORS

Hao Zheng, Xin Geng

ABSTRACT

Considering the distinctiveness of different group features in the sparse representation, a novel joint multi-task and weighted group sparsity (JMT-WGS) method is proposed. By weighting popular group sparsity, not only the representation coefficients from the same class over their associate dictionaries may share some similarity, but also the representation coefficients from different classes have enough diversity. The proposed method is cast into a multi-task framework with two-stage iteration. In the first stage, representation coefficient can be optimized by accelerated proximal gradient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expression databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithm. More... »

PAGES

266-275

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11704-016-5204-4

DOI

http://dx.doi.org/10.1007/s11704-016-5204-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084032739


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Southeast University", 
          "id": "https://www.grid.ac/institutes/grid.263826.b", 
          "name": [
            "Key Laboratory of Trusted Cloud Computing and Big Data Analysis, School of Information Engineering, Nanjing XiaoZhuang University, 211171, Nanjing, China", 
            "School of Computer Science and Engineering, Southeast University, 211189, Nanjing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zheng", 
        "givenName": "Hao", 
        "id": "sg:person.07575706553.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07575706553.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southeast University", 
          "id": "https://www.grid.ac/institutes/grid.263826.b", 
          "name": [
            "School of Computer Science and Engineering, Southeast University, 211189, Nanjing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Geng", 
        "givenName": "Xin", 
        "id": "sg:person.01160220403.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160220403.92"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1214/09-aos778", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002798591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1127647", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004607132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2522848.2531739", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006530478"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1835804.1835954", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011681513"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-24571-8_49", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016924487", 
          "https://doi.org/10.1007/978-3-642-24571-8_49"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11063-012-9214-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019784673", 
          "https://doi.org/10.1007/s11063-012-9214-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2005.00532.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021238034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2005.00532.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021238034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.imavis.2005.12.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023125946"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1077-3142(03)00081-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028049386"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1077-3142(03)00081-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028049386"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-15567-3_33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029674869", 
          "https://doi.org/10.1007/978-3-642-15567-3_33"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-15567-3_33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029674869", 
          "https://doi.org/10.1007/978-3-642-15567-3_33"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1014052.1014067", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037354096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2015.06.079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043332912"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0031-3203(02)00052-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048549649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1049/cje.2015.04.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056747723"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.895976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061157196"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.908962", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061157208"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/78.258082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061228470"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2006.884954", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061641625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2012.2205006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061643270"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2005.862083", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061650773"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2013.2295717", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718492"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2013.2297381", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718502"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2015.2422994", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2008.79", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/090763184", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062856455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s003614450037906x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062877747"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/10-aos850", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064391510"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1587/transfun.e98.a.1351", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068091626"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/fg.2015.7163082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079291888"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/afgr.2002.1004141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093311840"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icdm.2009.128", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093573501"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2009.5459169", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093817401"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvprw.2003.10057", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094236581"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2007.383084", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094251193"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2006.14", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094432379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/afgr.2000.840611", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094698009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/10001-000", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1108334319"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-04", 
    "datePublishedReg": "2017-04-01", 
    "description": "Considering the distinctiveness of different group features in the sparse representation, a novel joint multi-task and weighted group sparsity (JMT-WGS) method is proposed. By weighting popular group sparsity, not only the representation coefficients from the same class over their associate dictionaries may share some similarity, but also the representation coefficients from different classes have enough diversity. The proposed method is cast into a multi-task framework with two-stage iteration. In the first stage, representation coefficient can be optimized by accelerated proximal gradient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expression databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithm.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11704-016-5204-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7181075", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1356943", 
        "issn": [
          "2095-2228", 
          "2095-2236"
        ], 
        "name": "Frontiers of Computer Science", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "11"
      }
    ], 
    "name": "Facial expression recognition via weighted group sparsity", 
    "pagination": "266-275", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f08392d346ca4ff1b2166adc961d4e8e6499806bd627be86cc1ac06a080715cc"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11704-016-5204-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1084032739"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11704-016-5204-4", 
      "https://app.dimensions.ai/details/publication/pub.1084032739"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:57", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000347_0000000347/records_89807_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11704-016-5204-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11704-016-5204-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11704-016-5204-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11704-016-5204-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11704-016-5204-4'


 

This table displays all metadata directly associated to this object as RDF triples.

185 TRIPLES      21 PREDICATES      64 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11704-016-5204-4 schema:about anzsrc-for:17
2 anzsrc-for:1701
3 schema:author N11fc8d356f7b4eb9bdbbb545921734f8
4 schema:citation sg:pub.10.1007/978-3-642-15567-3_33
5 sg:pub.10.1007/978-3-642-24571-8_49
6 sg:pub.10.1007/s11063-012-9214-4
7 https://doi.org/10.1016/j.imavis.2005.12.021
8 https://doi.org/10.1016/j.neucom.2015.06.079
9 https://doi.org/10.1016/s0031-3203(02)00052-3
10 https://doi.org/10.1016/s1077-3142(03)00081-x
11 https://doi.org/10.1037/10001-000
12 https://doi.org/10.1049/cje.2015.04.009
13 https://doi.org/10.1109/34.895976
14 https://doi.org/10.1109/34.908962
15 https://doi.org/10.1109/78.258082
16 https://doi.org/10.1109/afgr.2000.840611
17 https://doi.org/10.1109/afgr.2002.1004141
18 https://doi.org/10.1109/cvpr.2006.14
19 https://doi.org/10.1109/cvpr.2007.383084
20 https://doi.org/10.1109/cvprw.2003.10057
21 https://doi.org/10.1109/fg.2015.7163082
22 https://doi.org/10.1109/iccv.2009.5459169
23 https://doi.org/10.1109/icdm.2009.128
24 https://doi.org/10.1109/tip.2006.884954
25 https://doi.org/10.1109/tip.2012.2205006
26 https://doi.org/10.1109/tit.2005.862083
27 https://doi.org/10.1109/tnnls.2013.2295717
28 https://doi.org/10.1109/tnnls.2013.2297381
29 https://doi.org/10.1109/tnnls.2015.2422994
30 https://doi.org/10.1109/tpami.2008.79
31 https://doi.org/10.1111/j.1467-9868.2005.00532.x
32 https://doi.org/10.1126/science.1127647
33 https://doi.org/10.1137/090763184
34 https://doi.org/10.1137/s003614450037906x
35 https://doi.org/10.1145/1014052.1014067
36 https://doi.org/10.1145/1835804.1835954
37 https://doi.org/10.1145/2522848.2531739
38 https://doi.org/10.1214/09-aos778
39 https://doi.org/10.1214/10-aos850
40 https://doi.org/10.1587/transfun.e98.a.1351
41 schema:datePublished 2017-04
42 schema:datePublishedReg 2017-04-01
43 schema:description Considering the distinctiveness of different group features in the sparse representation, a novel joint multi-task and weighted group sparsity (JMT-WGS) method is proposed. By weighting popular group sparsity, not only the representation coefficients from the same class over their associate dictionaries may share some similarity, but also the representation coefficients from different classes have enough diversity. The proposed method is cast into a multi-task framework with two-stage iteration. In the first stage, representation coefficient can be optimized by accelerated proximal gradient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expression databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithm.
44 schema:genre research_article
45 schema:inLanguage en
46 schema:isAccessibleForFree false
47 schema:isPartOf N38f88263828a44f6aadbb6b503038bfa
48 N3c8bf50f381f44868a566a21c5cdd33f
49 sg:journal.1356943
50 schema:name Facial expression recognition via weighted group sparsity
51 schema:pagination 266-275
52 schema:productId N2c3f0b8d55a443c0bb8ef92d2554e292
53 N634a02e54e494ffaaa17a74e6cb484ec
54 Nae1596cd2b524dca9dbfef9a21f6c815
55 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084032739
56 https://doi.org/10.1007/s11704-016-5204-4
57 schema:sdDatePublished 2019-04-11T09:57
58 schema:sdLicense https://scigraph.springernature.com/explorer/license/
59 schema:sdPublisher N49e686070fca4a9a97b401728356bae0
60 schema:url https://link.springer.com/10.1007%2Fs11704-016-5204-4
61 sgo:license sg:explorer/license/
62 sgo:sdDataset articles
63 rdf:type schema:ScholarlyArticle
64 N11fc8d356f7b4eb9bdbbb545921734f8 rdf:first sg:person.07575706553.15
65 rdf:rest Nb0912641618a4a778734396e819093fa
66 N2c3f0b8d55a443c0bb8ef92d2554e292 schema:name readcube_id
67 schema:value f08392d346ca4ff1b2166adc961d4e8e6499806bd627be86cc1ac06a080715cc
68 rdf:type schema:PropertyValue
69 N38f88263828a44f6aadbb6b503038bfa schema:issueNumber 2
70 rdf:type schema:PublicationIssue
71 N3c8bf50f381f44868a566a21c5cdd33f schema:volumeNumber 11
72 rdf:type schema:PublicationVolume
73 N49e686070fca4a9a97b401728356bae0 schema:name Springer Nature - SN SciGraph project
74 rdf:type schema:Organization
75 N634a02e54e494ffaaa17a74e6cb484ec schema:name dimensions_id
76 schema:value pub.1084032739
77 rdf:type schema:PropertyValue
78 Nae1596cd2b524dca9dbfef9a21f6c815 schema:name doi
79 schema:value 10.1007/s11704-016-5204-4
80 rdf:type schema:PropertyValue
81 Nb0912641618a4a778734396e819093fa rdf:first sg:person.01160220403.92
82 rdf:rest rdf:nil
83 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
84 schema:name Psychology and Cognitive Sciences
85 rdf:type schema:DefinedTerm
86 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
87 schema:name Psychology
88 rdf:type schema:DefinedTerm
89 sg:grant.7181075 http://pending.schema.org/fundedItem sg:pub.10.1007/s11704-016-5204-4
90 rdf:type schema:MonetaryGrant
91 sg:journal.1356943 schema:issn 2095-2228
92 2095-2236
93 schema:name Frontiers of Computer Science
94 rdf:type schema:Periodical
95 sg:person.01160220403.92 schema:affiliation https://www.grid.ac/institutes/grid.263826.b
96 schema:familyName Geng
97 schema:givenName Xin
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160220403.92
99 rdf:type schema:Person
100 sg:person.07575706553.15 schema:affiliation https://www.grid.ac/institutes/grid.263826.b
101 schema:familyName Zheng
102 schema:givenName Hao
103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07575706553.15
104 rdf:type schema:Person
105 sg:pub.10.1007/978-3-642-15567-3_33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029674869
106 https://doi.org/10.1007/978-3-642-15567-3_33
107 rdf:type schema:CreativeWork
108 sg:pub.10.1007/978-3-642-24571-8_49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016924487
109 https://doi.org/10.1007/978-3-642-24571-8_49
110 rdf:type schema:CreativeWork
111 sg:pub.10.1007/s11063-012-9214-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019784673
112 https://doi.org/10.1007/s11063-012-9214-4
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/j.imavis.2005.12.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023125946
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/j.neucom.2015.06.079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043332912
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/s0031-3203(02)00052-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048549649
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/s1077-3142(03)00081-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1028049386
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1037/10001-000 schema:sameAs https://app.dimensions.ai/details/publication/pub.1108334319
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1049/cje.2015.04.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056747723
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1109/34.895976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157196
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1109/34.908962 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157208
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1109/78.258082 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061228470
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1109/afgr.2000.840611 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094698009
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1109/afgr.2002.1004141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093311840
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1109/cvpr.2006.14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094432379
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1109/cvpr.2007.383084 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094251193
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1109/cvprw.2003.10057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094236581
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1109/fg.2015.7163082 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079291888
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1109/iccv.2009.5459169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093817401
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1109/icdm.2009.128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093573501
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1109/tip.2006.884954 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061641625
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1109/tip.2012.2205006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061643270
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1109/tit.2005.862083 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061650773
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1109/tnnls.2013.2295717 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718492
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1109/tnnls.2013.2297381 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718502
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1109/tnnls.2015.2422994 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718838
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1109/tpami.2008.79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743675
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1111/j.1467-9868.2005.00532.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1021238034
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1126/science.1127647 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004607132
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1137/090763184 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062856455
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1137/s003614450037906x schema:sameAs https://app.dimensions.ai/details/publication/pub.1062877747
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1145/1014052.1014067 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037354096
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1145/1835804.1835954 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011681513
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1145/2522848.2531739 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006530478
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1214/09-aos778 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002798591
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1214/10-aos850 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064391510
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1587/transfun.e98.a.1351 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068091626
181 rdf:type schema:CreativeWork
182 https://www.grid.ac/institutes/grid.263826.b schema:alternateName Southeast University
183 schema:name Key Laboratory of Trusted Cloud Computing and Big Data Analysis, School of Information Engineering, Nanjing XiaoZhuang University, 211171, Nanjing, China
184 School of Computer Science and Engineering, Southeast University, 211189, Nanjing, China
185 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...