[go: up one dir, main page]

Ng et al., 2018 - Google Patents

Incremental hash-bit learning for semantic image retrieval in nonstationary environments

Ng et al., 2018

Document ID
6096194484985797817
Author
Ng W
Tian X
Pedrycz W
Wang X
Yeung D
Publication year
Publication venue
IEEE transactions on cybernetics

External Links

Snippet

Images are uploaded to the Internet over time which makes concept drifting and distribution change in semantic classes unavoidable. Current hashing methods being trained using a given static database may not be suitable for nonstationary semantic image retrieval …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861Retrieval from the Internet, e.g. browsers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6261Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Yu et al. An active three-way clustering method via low-rank matrices for multi-view data
Zhou et al. Few-shot class-incremental learning by sampling multi-phase tasks
Liu et al. Query-adaptive reciprocal hash tables for nearest neighbor search
Ng et al. Incremental hash-bit learning for semantic image retrieval in nonstationary environments
Zheng et al. Adaptive partial multi-view hashing for efficient social image retrieval
Liu et al. Structure sensitive hashing with adaptive product quantization
Liu et al. Compact kernel hashing with multiple features
JP5235666B2 (en) Associative matrix method, system and computer program product using bit-plane representation of selected segments
Ng et al. Incremental hashing for semantic image retrieval in nonstationary environments
CN113779219A (en) A Question Answering Method Combined with Text Hyperbolic Segmentation Knowledge Embedding Multiple Knowledge Graphs
Tian et al. Complementary incremental hashing with query-adaptive re-ranking for image retrieval
Wang et al. Learning to hash with partial tags: Exploring correlation between tags and hashing bits for large scale image retrieval
CN115795065B (en) Cross-modal multimedia data retrieval method and system based on weighted hash codes
US11244015B1 (en) Projecting queries into a content item embedding space
Deng et al. Adaptive multi-bit quantization for hashing
Weng et al. Online hashing with bit selection for image retrieval
Li et al. Hashing with dual complementary projection learning for fast image retrieval
Zhang et al. Deep unsupervised self-evolutionary hashing for image retrieval
Guo et al. Pilora: Prototype guided incremental lora for federated class-incremental learning
Tian et al. Concept preserving hashing for semantic image retrieval with concept drift
Kan et al. Zero-shot learning to index on semantic trees for scalable image retrieval
Ng et al. Bit-wise attention deep complementary supervised hashing for image retrieval
Zhou et al. Hierarchical task-incremental learning with feature-space initialization inspired by neural collapse
Avrithis Quantize and Conquer: A dimensionality-recursive solution to clustering, vector quantization, and image retrieval
Dai et al. Joint multilabel classification and feature selection based on deep canonical correlation analysis