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03615nam a22005535i 4500 |
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978-3-662-45000-0 |
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DE-He213 |
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20210617124234.0 |
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cr nn 008mamaa |
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150105s2014 gw | s |||| 0|eng d |
| 020 |
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|a 9783662450000
|9 978-3-662-45000-0
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|a 10.1007/978-3-662-45000-0
|2 doi
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|a Q337.5
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|a TK7882.P3
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|a 006.4
|2 23
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|a Huang, Yongzhen.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Feature Coding for Image Representation and Recognition
|h [electronic resource] /
|c by Yongzhen Huang, Tieniu Tan.
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| 250 |
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|a 1st ed. 2014.
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| 264 |
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1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2014.
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| 300 |
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|a XIII, 74 p. 36 illus., 32 illus. in color.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 505 |
0 |
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|a 1. Introduction -- 2. Taxonomy -- 3. Representative Feature Coding Algorithms -- 4. Evolution of Feature Coding -- 5. Experimental Study of Feature Coding -- 6. Enhancement via Integrating Spatial Information -- 7. Enhancement via Integrating High Order Coding Information -- 8. Conclusion.
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| 520 |
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|a This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) Discuss the applications of feature coding in different visual tasks, analyze the influence of some key factors in feature coding with intensive experimental studies; (5) Provide the suggestions of how to apply different feature coding methods and forecast the potential directions for future work on the topic. It is suitable for students, researchers, practitioners interested in object recognition.
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| 650 |
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0 |
|a Pattern recognition.
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| 650 |
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|a Optical data processing.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Algorithms.
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| 650 |
1 |
4 |
|a Pattern Recognition.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
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| 650 |
2 |
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|a Image Processing and Computer Vision.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I22021
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| 650 |
2 |
4 |
|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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| 650 |
2 |
4 |
|a Algorithm Analysis and Problem Complexity.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I16021
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| 700 |
1 |
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|a Tan, Tieniu.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783662450017
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783662449998
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| 830 |
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-662-45000-0
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
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