Neural Codes for Image Retrieval

Computer Vision – ECCV 2014 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-10590-1_38 ◽

2014 ◽

pp. 584-599 ◽

Author(s):

Artem Babenko ◽

Anton Slesarev ◽

Alexandr Chigorin ◽

Victor Lempitsky

Keyword(s):

Image Retrieval ◽

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Convolutional neural codes for image retrieval

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific ◽

10.1109/apsipa.2014.7041557 ◽

2014 ◽

Author(s):

Xin-Yu Ou ◽

He-Fei Ling ◽

Ling-Yu Yan ◽

Mao-Lin Liu

Keyword(s):

Image Retrieval ◽

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Our understanding of neural codes rests on Shannon's foundations

Behavioral and Brain Sciences ◽

10.1017/s0140525x19001249 ◽

2019 ◽

Vol 42 ◽

Author(s):

Charles R. Gallistel

Keyword(s):

Brain Structure ◽

Neural Tissue ◽

Neural Codes ◽

Flow Of Information

Abstract Shannon's theory lays the foundation for understanding the flow of information from world into brain: There must be a set of possible messages. Brain structure determines what they are. Many messages convey quantitative facts (distances, directions, durations, etc.). It is impossible to consider how neural tissue processes these numbers without first considering how it encodes them.

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Semantic Image Retrieval and Clustering for Supporting Domain-Specific Bridge Component and Defect Classification

Construction Research Congress 2020 ◽

10.1061/9780784482858.087 ◽

2020 ◽

Author(s):

Peter Cheng-Yang Liu ◽

Nora El-Gohary

Keyword(s):

Image Retrieval ◽

Defect Classification ◽

Semantic Image Retrieval ◽

Domain Specific

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Similarity-based Image Retrieval considering Artifacts from Plural Key Images by Self-Organizing Map with Refractoriness

IEICE Proceeding Series ◽

10.15248/proc.1.9 ◽

2014 ◽

Vol 1 ◽

pp. 9-12

Author(s):

Yu MORIMOTO ◽

Yuko OSANA

Keyword(s):

Image Retrieval ◽

Self Organizing Map ◽

Self Organizing

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A New Content-Based Image Retrieval Using the Multidimensional Generalization of Wald-Wolfowitz Runs Test

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.129.94 ◽

2009 ◽

Vol 129 (1) ◽

pp. 94-102 ◽

Author(s):

Thurdsak Leauhatong ◽

Kazuhiko Hamamoto ◽

Kiyoaki Atsuta ◽

Shozo Kondo

Keyword(s):

Image Retrieval ◽

Content Based Image Retrieval ◽

Multidimensional Generalization

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Locality Preserving Hashing based on Random Rotation and Offsets of PCA in Image Retrieval

10.23940/ijpe.18.11.p6.26012611 ◽

2018 ◽

Author(s):

Shan Zhao

Keyword(s):

Image Retrieval ◽

Locality Preserving ◽

Random Rotation

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Narrowing the semantic-gap using multi-modal ontology for semantic image retrieval

Advances in Communication Technology and Application ◽

10.2495/cta140761 ◽

2014 ◽

Author(s):

R.I. Minu ◽

K.K. Thyagharajan

Keyword(s):

Image Retrieval ◽

Semantic Gap ◽

Semantic Image Retrieval ◽

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Fault map query using content-based image retrieval

Information and Communication Technology for Education ◽

10.2495/icte130431 ◽

2014 ◽

Author(s):

Jie Ma ◽

Xiaomei Zhang ◽

Zhiwei Zhang

Keyword(s):

Image Retrieval ◽

Content Based Image Retrieval

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Efficient Image Retrieval approach for Large-scale Chest X Ray data using Hand-Crafted Features and Machine Learning Algorithms

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i11.890896 ◽

2018 ◽

Vol 6 (11) ◽

pp. 890-896

Author(s):

Irene Getzi S ◽

D. Christopher Durairaj ◽

V Joseph Raj

Keyword(s):

Machine Learning ◽

Image Retrieval ◽

Large Scale ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

X Ray ◽

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A Comprehensive Survey on Semantic based Image Retrieval Systems for Cyber Forensics

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i8.245250 ◽

2018 ◽

Vol 6 (8) ◽

pp. 245-250

Author(s):

Ramesh Babu P ◽

E. Sreenivasa Reddy

Keyword(s):

Image Retrieval ◽

Cyber Forensics ◽

Retrieval Systems ◽

Comprehensive Survey

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