Computer vision methods for fast image classification and retrieval:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer
[2020]
|
| Schriftenreihe: | Studies in computational intelligence
Volume 821 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030916590&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Abstract: | The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as ‘hand-crafted features.’ It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images. Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book’s main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions |
| Umfang: | ix, 137 Seiten Illustrationen, Diagramme (farbig) |
| ISBN: | 9783030121945 9783030121976 |
Internformat
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| 490 | 1 | |a Studies in computational intelligence |v Volume 821 | |
| 520 | 3 | |a The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as ‘hand-crafted features.’ It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images. Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book’s main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions | |
| 653 | 0 | |a Engineering | |
| 653 | 0 | |a Computer vision | |
| 653 | 0 | |a Computational Intelligence | |
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-030-12195-2 |
| 830 | 0 | |a Studies in computational intelligence |v Volume 821 |w (DE-604)BV020822171 |9 821 | |
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Datensatz im Suchindex
| _version_ | 1819315870608916480 |
|---|---|
| adam_text | Contents 1 Introduction................................................................................................... References........................................................................................................ 1 3 2 Feature Detection.......................................................................................... 2.1 Local Features....................... .................................... .......................... 2.1.1 Scale-Invariant Feature Transform (SIFT) . ........................... 2.1.2 Speed-Up Robust Features (SURF)........................................ 2.2 Edge Detection..................................................■.·..■............................. 2.2.1 Canny Edge Detection.............................................................. 2.3 Blob Detection and Blob Extraction............... 2.4 Clustering Algorithms.......................................... 2.4.1 Äľ-means Clustering Algorithm................................................ 2.4.2 Mean Shift Clustering Algorithm..................... 2.5 Segmentation............................................................. 2.6 Global Features............................................... 2.6.1 Colour and Edge Directivity CEDD Descriptor................... 2.7 Summary and Discussion............ .................. References........................................................ 7 7 7 9 12 16 18 19 20 20 22 26 27 29 29 3 Image Indexing Techniques............................................... 3.1 Image Classification by Fuzzy Rules
.................................................. 3.1.1 Boosting-Generated Simple Fuzzy Classifiers...................... 3.1.2 Classification of a Query Image............................................. 3.1.3 Experiments....................... 3.1.4 Conclusions............................ .. ................. . . . ...................... 3.2 Salient Object Detectorand Descriptor by Edge Crawler................ 3.2.1 System for Content-Based Image Retrieval................... 3.2.2 Experimental Results................................. 3.2.3 Conclusions...................................................................... 33 34 36 40 41 42 44 47 49 55 vii
viii Contents 3.3 Fast Two-Level Image Indexing...................................................... 3.3.1 Hash Generation..................................................................... 3.3.2 Structure of the ProposedDescriptor Index.......................... 3.3.3 Index Search Process.............................................................. 3.3.4 Experimental Results.............................................................. 3.3.5 Conclusions.............................................................................. 3.4 Image Colour Descriptor..................................................................... 3.4.1 Method Description................................................................ 3.4.2 Color Descriptor..................................................................... 3.4.3 Colour Relationship Sub-descriptor...................................... 3.4.4 Descriptor Comparison........................................................... 3.4.5 Experimental Results.............................................................. 3.4.6 Conclusions.............................................................................. 3.5 Fast Dictionary Matching................................................................... 3.5.1 Description of the Problem..................................................... 3.5.2 Method Description................................................................. 3.5.3 Comparison Between Descriptorsand Dictionary................. 3.5.4 Matching Sets of Keypoints.................................................. 3.5.5
Experimental Results.............................................................. 3.5.6 Conclusions............................................................................... 3.6 Summary and Discussion................................................................... References...................................................................................................... 55 56 58 58 60 63 64 65 66 67 67 68 71 71 72 73 73 74 76 78 79 80 4 Novel Methods forImage Description...................... 4.1 Algorithm for Discontinuous Edge Description................................ 4.1.1 Proposed Approach.............................................................. · 4.1.2 Experimental Results........................... .............. .................. 4.1.3 Conclusions.......................................................... · 4.2 Interest Point Localization Based on theGestalt Laws.................... 4.2.1 Problem Description ............................ 4.2.2 Method Description.................. ........................................... 4.2.3 Experiments ............................................................ · 4.2.4 Conclusions............................................. ................... ............. 4.3 Summary and Discussion................................ References......... .............................. 83 83 86 90 93 95 96 97 102 103 103 104 5 Image Retrieval andClassification in Relational Databases............... 107 5.1 Bag of Features Image Classificationin RelationalDatabases .... 108 5.1.1 System Architecture andRelationalDatabase
Structure .. . 109 5.1.2 Numerical Simulations............... ............................................ 112 5.1.3 Conclusions......... ............... 113
Contents ix 5.2 Bag of Features Image Retrieval inRelational Databases................ 114 5.2.1 Description of the ProposedSystem............................. 114 5.2.2 Numerical Experiments................................................. 117 5.2.3 Conclusions................................................................................ 118 5.3 Database Indexing System Based on Boosting and Fuzzy Sets............................................................. .......................................... 118 5.3.1 Building Visual Index................ 119 5.3.2 Proposed Database Framework............................................... 120 5.3.3 Numerical Simulations............................................................. 124 5.3.4 Conclusions..................... 126 5.4 Database Retrieval System Based on the CEDD Descriptor............ 126 5.4.1 Simulation Environment........................................................... 130 5.4.2 Conclusions................................................................................ 133 5.5 Summary and Discussion..................................................................... 133 References.............................................................................................. 134 6 Concluding Remarks andPerspectives in Computer Vision................ 137
|
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| id | DE-604.BV045532509 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:29:34Z |
| institution | BVB |
| isbn | 9783030121945 9783030121976 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030916590 |
| oclc_num | 1088569606 |
| open_access_boolean | |
| owner | DE-11 DE-473 DE-BY-UBG |
| owner_facet | DE-11 DE-473 DE-BY-UBG |
| physical | ix, 137 Seiten Illustrationen, Diagramme (farbig) |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Scherer, Rafał Computer vision methods for fast image classification and retrieval Studies in computational intelligence |
| title | Computer vision methods for fast image classification and retrieval |
| title_auth | Computer vision methods for fast image classification and retrieval |
| title_exact_search | Computer vision methods for fast image classification and retrieval |
| title_full | Computer vision methods for fast image classification and retrieval Rafał Scherer |
| title_fullStr | Computer vision methods for fast image classification and retrieval Rafał Scherer |
| title_full_unstemmed | Computer vision methods for fast image classification and retrieval Rafał Scherer |
| title_short | Computer vision methods for fast image classification and retrieval |
| title_sort | computer vision methods for fast image classification and retrieval |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030916590&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT schererrafał computervisionmethodsforfastimageclassificationandretrieval |