Knowledge transfer between computer vision and text mining: similarity-based learning approaches
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[Cham]
Springer
2016
|
| Schriftenreihe: | Advances in computer vision and pattern recognition
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xxiv, 250 Seiten Illustrationen, Diagramme (teilweise farbig) |
| ISBN: | 9783319303659 |
| ISSN: | 2191-6586 |
Internformat
MARC
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| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Image Processing and Computer Vision | |
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Datensatz im Suchindex
| _version_ | 1804176236924108800 |
|---|---|
| adam_text | Contents
1 Motivation and Overview. .......................................... 1
1.1 Introduction.................................................... 1
1.2 Knowledge Transfer between Image and Text........................ 2
1.3 Overview and Organization........................................ 7
References............................................................ 11
2 Learning based on Similarity........................................ 15
2.1 Introduction. .................................................. 15
2.2 Nearest Neighbor Approach....................................... 17
2.3 Local Learning.................................................. 20
2.4 Kernel Methods.................................................. 22
2.4.1 Mathematical Preliminaries............................... 22
2.4.2 Overview of Kernel Classifiers........................... 24
2.43 Kernel Functions........................................ 26
2.4.4 Kernel Normalization .................................... 28
2.4.5 Generic Kernel Algorithm............................... 29
2.4.6 Multiple Kernel Learning............................... 29
2.5 Ouster Analysis...................................* . ....... 30
2.5-1 K-Means Clustering . .................................... 32
2.5.2 Hierarchical Clustering.................................. 33
References............................................................ 35
Part I Knowledge Transfer from Text Mining to Computer Vision
3 State-of-the-Art Approaches for Image Classification.................. 41
3.1 Introduction.................................................. 41
3.2 Image Distance Measures....................................... 42
3.2.1 Color Image Distances.................................... 42
3.2.2 Grayscale Image Distances................................ 44
3.2.3 Earth Mover s Distance................................... 44
3.2.4 Tangent Distance......................................... 44
3.2.5 Shape Match Distance..................................... 45
XI
Contents
xii
3.3 Patch-based Techniques......................................... 45
3.4 Image Descriptors.............................................. 46
3.5 Bag of Visual Words............................................ 47
3.5.1 Encoding Spatial Information............................ 48
3.6 Deep Learning.................................................. 49
References........................................................... 50
4 Local Displacement Estimation of Image Patches and Textons .... 53
4.1 Introduction................................................... 53
4.2 Local Patch Dissimilarity...................................... 54
4.2.1 Extending Rank Distance to Images....................... 54
4.2.2 Local Patch Dissimilarity Algorithm..................... 56
4.2.3 LPD Algorithm Optimization.............................. 58
4.3 Properties of Local Patch Dissimilarity........................ 60
4.4 Experiments and Results........................................ 61
4.4.1 Data Sets Description................................... 61
4.4.2 Learning Methods........................................ 62
4.4.3 Parameter Tuning........................................ 64
4.4.4 Baseline Experiment..................................... 67
4.4.5 Kernel Experiment....................................... 72
4.4.6 Difficult Experiment.................................... 74
4.4.7 Filter-based Nearest Neighbor Experiment................ 75
4.4.8 Local Learning Experiment............................... 78
4.4.9 Birds Experiment........................................ 79
4.5 Local Tex ton Dissimilarity.................................... 81
4.5.1 Texton-based Methods.................................... 81
4.5.2 Texture Features........................................ 82
4.5.3 Local Texton Dissimilarity Algorithm.................... 83
4.6 Texture Experiments and Results................................ 85
4.6.1 Data Sets Description................................... 86
4.6.2 Learning Methods........................................ 88
4.6.3 Brodatz Experiment...................................... 89
4.6.4 UTUCTex Experiment...................................... 91
4.6.5 Biomass Experiment...................................... 95
4.7 Discussion..................................................... 96
References........................................................... 96
5 Object Recognition with the Bag of Visual Words Model............... 99
5.1 Introduction................................................... 99
5.2 Bag of Visual Words Model..................................... 101
5.3 PQ Kernel for Visual Word Histograms.......................... 103
5.4 Spatial Non-Alignment Kernel.................................. 107
5.4.1 Translation and Size Invariance........................ 109
5.5 Object Recognition Experiments................................ 110
5.5.1 Data Sets Description.................................. 112
5.5.2 Implementation and Evaluation Procedure................ 113
Contents
xiü
5.5.3 PQ Kemel Results on Pascal VOC Experiment............. 115
5.5.4 PQ Kemel Results on Birds Experiment.................. 118
5.5.5 SNAK Parameter Tuning................................. 119
5.5.6 SNAK Results on Pascal VOC Experiment................. 120
5.5.7 SNAK Results on Birds Experiment...................... 121
5.6 Bag of Visual Words for Facial Expression Recognition........ 122
5.7 Local Learning............................................... 125
5.8 Facial Expression Recognition Experiments.................... 125
5.8.1 Data Set Description.................................. 125
5.8.2 Implementation........................................ 127
5.8.3 Parameter Tuning and Results.......................... 127
5.9 Discussion................................................... 129
References....................................................... 130
Part II Knowledge Transfer from Computer Vision to Text Mining
6 State-of-tJhe-Art Approaches for String and Text Analysis.......... 135
6.1 Introduction................................................. 135
6.2 String Distance Measures..................................... 136
6.2.1 Hamming Distance...................................... 136
6.2.2 Edit Distance......................................... 137
6.2.3 Rank Distance......................................... 137
6.3 Computational Biology........................................ 139
6.3.1 Sequencing and Comparing DNA.......................... 139
6.3.2 Phylogenetic Analysis................................. 140
6.4 Text Mining.................................................. 141
6.4.1 String Kernels........................................ 142
References........................................................ 144
7 Local Rank Distance................................................ 149
7.1 Introduction................................................. 149
7.2 Approach..................................................... 151
7.3 Local Rank Distance Definition............................... 153
7.4 Local Rank Distance Algorithm................................ 155
7.5 Properties of Local Rank Distance............................ 158
7.6 Local Rank Distance Sequence Aligners........................ 161
7.6.1 Indexing Strategies and Efficiency Improvements....... 163
7.7 Experiments and Results...................................... 165
7.7.1 Data Sets Description................................. 165
7.7.2 Phylogenetic Analysis................................. 167
7.7.3 DNA Comparison........................................ 171
7.7.4 Alignment in the Presence of Contaminated Reads...... 172
7.7.5 Clustering an Unknown Organism........................ 180
7.7.6 Time Evaluation of Sequence Aligners.................. 184
7.7.7 Experiment on Vibrio Species.......................... 185
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XJV
7.8 Discussion
References ....
8 Native Language Identification with String Kernels.......
8.1 Introduction......................................
8.2 Related Work......................................
8.2.1 Native Language Identification.............
8.2.2 Methods that Work at the Character Level . . .
8.3 Similarity Measures for Strings ..................
8.3.1 String Kernels.............................
8.3.2 Kernel based on Local Rank Distance........
8.4 Learning Methods..................................
8.5 Experiments.......................................
8.5.1 Data Sets Description......................
8.5.2 Parameter Tuning and Implementation Choices
8.5.3 Experiment on TOEFL11 Corpus...............
8.5.4 Experiment on ICLE Corpus..................
8.5.5 Experiment on TOEFLILBig Corpus............
8.5.6 Cross-Corpus Experiment....................
8.5.7 Experiment on ALC Subset Corpus............
8.5.8 Experiment on ASK Corpus...................
8.6 Language Transfer Analysis........................
8.7 Discussion........................................
References...............................................
9 Spatial Information in Text Categorization...............
9.1 Introduction......................................
9.2 Related Work......................................
9.3 Methods to Encode Spatial Information.............
9.3.1 Spatial Pyramid for Text................. . . -
9.3.2 Spatial Non-Alignment Kernel for Text......
9.4 Experiments.......................................
9.4.1 Data Set Description.......................
9.4.2 Implementation Choices ....................
9.4.3 Evaluation Procedure.......................
9.4.4 Experiment on Reuters-21578 Corpus.........
9.5 Discussion........................................
References...............................................
10 Conclusions..............................................
10.1 Discussion and Conclusions..........................
References................................................
Index
Advances in Computer Vision and Pattern Recognition
Sing Bing Kang Series Editor
Radu Tudor lonescu • Marius Popescu
Knowledge Transfer between Computer Vision and Text Mining
Similarity-based learning Approaches
This ground-breaking text/reference diverges from the traditional view that computer
vision (for image analysis) and string processing (for text mining) are separate and
unrelated fields of study, propounding that images and text can be treated in a similar
manner for the purposes of information retrieval, extraction and classification.
Highlighting the benefits of knowledge transfer between the two disciplines, the
text presents a range of novel similarity-based learning techniques founded on this
approach.
Topics and Features:
• D escribes a varie ty of similar ity-based learning approaches, including nearest
neighbor models, local learning, kernel methods, and clustering algorithms
* Presents a nearest neighbor model based on a novel dissimilarity for images, and
applies this for handwritten digit recognition and texture analysis
♦ Discusses a novel kernel for (visual) word histograms, as well as several
kernels based on pyramid representation, and uses these for fecial expression
recognition and text categorization by topic
* Introduces an approach based on string kernels for native language
identification
• Contains links for downloading relevant open source code
« With a foreword by Prof. Florentina Hristea
This unique work will be of great benefit to researchers, postgraduate and advanced
undergraduate students involved in machine learning, data science, text mining and
computer vision.
Dr. Rad« Tudor lonescu is an Assistant Professor in the D epartment of Computer Science
at the University of Bucharest, Romania. Dr. Marte Popescu is an Associate Professor
|
| any_adam_object | 1 |
| author | Ionescu, Radu Tudor Popescu, Marius |
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| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| illustrated | Illustrated |
| indexdate | 2024-07-10T07:28:59Z |
| institution | BVB |
| isbn | 9783319303659 |
| issn | 2191-6586 |
| language | English |
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| spelling | Ionescu, Radu Tudor Verfasser (DE-588)1102067482 aut Knowledge transfer between computer vision and text mining similarity-based learning approaches Radu Tudor Ionescu, Marius Popescu [Cham] Springer 2016 © 2016 xxiv, 250 Seiten Illustrationen, Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Advances in computer vision and pattern recognition 2191-6586 Image processing Computer Science Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Data Mining and Knowledge Discovery Informatik Data Mining (DE-588)4428654-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Informatik (DE-588)4026894-9 gnd rswk-swf Informatik (DE-588)4026894-9 s Data Mining (DE-588)4428654-5 s Künstliche Intelligenz (DE-588)4033447-8 s Bildverarbeitung (DE-588)4006684-8 s DE-604 Popescu, Marius Verfasser (DE-588)1102067377 aut Erscheint auch als Online-Ausgabe 978-3-319-30367-3 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028979823&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028979823&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Ionescu, Radu Tudor Popescu, Marius Knowledge transfer between computer vision and text mining similarity-based learning approaches Image processing Computer Science Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Data Mining and Knowledge Discovery Informatik Data Mining (DE-588)4428654-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd Informatik (DE-588)4026894-9 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4033447-8 (DE-588)4006684-8 (DE-588)4026894-9 |
| title | Knowledge transfer between computer vision and text mining similarity-based learning approaches |
| title_auth | Knowledge transfer between computer vision and text mining similarity-based learning approaches |
| title_exact_search | Knowledge transfer between computer vision and text mining similarity-based learning approaches |
| title_full | Knowledge transfer between computer vision and text mining similarity-based learning approaches Radu Tudor Ionescu, Marius Popescu |
| title_fullStr | Knowledge transfer between computer vision and text mining similarity-based learning approaches Radu Tudor Ionescu, Marius Popescu |
| title_full_unstemmed | Knowledge transfer between computer vision and text mining similarity-based learning approaches Radu Tudor Ionescu, Marius Popescu |
| title_short | Knowledge transfer between computer vision and text mining |
| title_sort | knowledge transfer between computer vision and text mining similarity based learning approaches |
| title_sub | similarity-based learning approaches |
| topic | Image processing Computer Science Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Data Mining and Knowledge Discovery Informatik Data Mining (DE-588)4428654-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd Informatik (DE-588)4026894-9 gnd |
| topic_facet | Image processing Computer Science Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Data Mining and Knowledge Discovery Informatik Data Mining Künstliche Intelligenz Bildverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028979823&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028979823&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT ionescuradutudor knowledgetransferbetweencomputervisionandtextminingsimilaritybasedlearningapproaches AT popescumarius knowledgetransferbetweencomputervisionandtextminingsimilaritybasedlearningapproaches |