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Content-based image retrieval: History, Applications, Research & Science

Content-based image retrieval, also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey for a scientific overview of the CBIR field).…

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Content-based image retrieval topic overview

The analysis highlights History, Applications, Research and Science as prominent areas in the source structure around Content-based image retrieval.

Related topics
44
Source areas
11
Connected nodes
55
Extracted relationships
187
Concept neighborhoods
20
Bridge connections
55

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Content comparison using image distance measures · 10 topics
Applications · 8 topics
Overview · 8 topics
History · 7 topics
Techniques · 5 topics
Comparison with metadata searching · 1 topics
Image retrieval evaluation · 1 topics
Image retrieval in CBIR system simultaneously by different techniques · 1 topics
Relevant research papers · 1 topics
Technical progress · 1 topics
Vulnerabilities, attacks and defenses · 1 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Comparison with metadata searching

History

Technical progress

Techniques

Content comparison using image distance measures

Vulnerabilities, attacks and defenses

Image retrieval evaluation

Image retrieval in CBIR system simultaneously by different techniques

Applications

Relevant research papers

  • JISC Joint Information Systems Committee

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Content-based image retrieval connects Entity context

The extracted context around Content-based image retrieval shows recurring relationship patterns in the source. For example, Content-based image retrieval → Accurate Image Annotation, Adaptively Browsing Image Databases, Advances, Alexis, Algorithm, Amsaleg, An Interactive Face Retrieval, Arandjelovic, Ardizzoni, Art, Automatic Face Recognition, Automatic Linguistic Indexing, Automatic Video Content Indexing, Bartolini, Berg, Browsing Engine, Challenges, Ciaccia, Conceptual Approach, Content Another extracted example is Content-based image retrieval → An, As, For, Many, Search, Similarity Models, The. Use these groups to spot repeated connection types before inspecting the individual relationships.

Content-based image retrieval

Top relations

related to Relevant research papers · 154
Content-based image retrieval → Accurate Image Annotation, Adaptively Browsing Image Databases, Advances, Alexis, Algorithm, Amsaleg, An Interactive Face Retrieval, Arandjelovic, Ardizzoni, Art, Automatic Face Recognition, Automatic Linguistic Indexing, Automatic Video Content Indexing, Bartolini, Berg, Browsing Engine, Challenges, Ciaccia, Conceptual Approach, Content
related to Content comparison using image distance measures · 7
Content-based image retrieval → An, As, For, Many, Search, Similarity Models, The
related to history · 4
Content-based image retrieval → Japanese Electrotechnical Laboratory, Since, The, Toshikazu Kato

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

image images cbir search query retrieval content texture systems based content-based example shape also techniques user methods may developed distance

Content-based image retrieval relationships Subject–Predicate–Object triples

TTTA extracted 187 structured relationships around Content-based image retrieval. Examples in this analysis include keywords → instance of → means that the search analyzes the contents of the image rather than the metadata and statistics → instance of → and algorithms that are used originate from fields. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
keywordsinstance ofmeans that the search analyzes the contents of the image rather than the metadata0.80text
tagsinstance ofmeans that the search analyzes the contents of the image rather than the metadata0.80text
or descriptions associated with the imageinstance ofmeans that the search analyzes the contents of the image rather than the metadata0.80text
statisticsinstance ofand algorithms that are used originate from fields0.80text
pattern recognitioninstance ofand algorithms that are used originate from fields0.80text
signal processinginstance ofand algorithms that are used originate from fields0.80text
and computer vision..mw-parser-output .vanchorinstance ofand algorithms that are used originate from fields0.80text
colorinstance ofAn image distance measure compares the similarity of two images in various dimensions0.80text
textureinstance ofAn image distance measure compares the similarity of two images in various dimensions0.80text
shapeinstance ofAn image distance measure compares the similarity of two images in various dimensions0.80text
and othersinstance ofAn image distance measure compares the similarity of two images in various dimensions0.80text
silkyinstance ofThe problem is in identifying patterns of co-pixel variation and associating them with particular classes of textures0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Content-based image retrieval bring nearby vocabulary together. In this analysis, examples include Retrieval, Visual and Using. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Content-based image retrieval
    • Retrieval
    • Visual
    • Using
    • See
    • Query
    • Based
    • Searching
    • Content
    • Databases
    • Information
    • Qbic
    • Images
  • content-based image retrieval
    • Retrieval
    • Visual
    • Using
    • See
    • Images
    • Query
    • Based
    • Searching
    • Search
    • Content
    • Databases
    • Information
  • qbic
    • Large
    • See
    • System
    • Query
    • Searching
    • Information
    • Using
    • Visual
    • Retrieval
    • Developed
    • Techniques
    • Search
  • image retrieval
    • Using
    • Visual
    • Images
    • Retrieval
    • See
    • Search
    • Based
    • Searching
    • Large
    • System
    • Include
    • Query
  • digital images
    • Query
    • Visual
    • Search
    • Describe
    • Large
    • Retrieval
    • Using
    • Example
    • Based
    • Searching
    • Information
    • Qbic
  • concept-based image indexing
    • Images
    • Retrieval
    • Search
    • System
    • Based
    • Query
    • Shapes
    • Distance
    • Example
    • Texture
    • Using
    • Visual
  • image meta search
    • Images
    • Retrieval
    • Search
    • Example
    • Based
    • System
    • Query
    • Shapes
    • Using
    • Visual
    • Distance
    • Texture
  • query by example
    • Retrieval
    • Qbic
    • Images
    • Using
    • Visual
    • Distance
    • Include
    • System
    • Example
    • Query
    • May
    • Search

Connections between topic areas Semantic bridges

For Content-based image retrieval, one of the stronger structural bridges in this analysis connects Content-based image retrieval with Content comparison using image distance measures. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Content-based image retrieval — Content comparison using image distance measures · splits 45 ⟂ 11
Content-based image retrieval — Overview · splits 47 ⟂ 9
Content-based image retrieval — Applications · splits 47 ⟂ 9
Content-based image retrieval — History · splits 48 ⟂ 8
Content-based image retrieval — Techniques · splits 50 ⟂ 6

Map overview Semantic statistics

Content-based image retrieval

Nodes56
Edges55
Triples187
Avg. degree1.96
Density0.035714
Components1

Source & methodology

TTTA analyzes the structure around Content-based image retrieval to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications, Research & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Content-based image retrieval · EN edition · Analysis: TopicsToTalkAbout

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