Content-based image retrieval
Content-based image retrieval, also known as query by image content 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). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing).
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- enContent-based image retrieval, also known as query by image content 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). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing).
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- enAugust 2017
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- enyes
- Has abstract
- enContent-based image retrieval, also known as query by image content 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). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing). "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness. Having humans manually annotate images by entering keywords or metadata in a large database can be time-consuming and may not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success. "Keywords also limit the scope of queries to the set of predetermined criteria." and, "having been set up" are less reliable than using the content itself.
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- enContent-based image retrieval
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- Art collections
- Automatic image annotation
- BLOB
- Category:Applications of artificial intelligence
- Category:Applications of computer vision
- Category:Image search
- Color histogram
- Computer vision
- Concept-based image indexing
- Crime prevention
- Database
- Digital image
- Document classification
- Edge detection
- Electrotechnical Laboratory
- Facial recognition system
- File:Principe cbir.png
- Fourier transform
- GazoPa
- Generative adversarial network
- Geographic information systems
- Image moment
- Image retrieval
- Image texture
- Intellectual property
- Joint Information Systems Committee
- Learning to rank
- List of CBIR engines
- Machine learning
- Macroglossa Visual Search
- Medical diagnosis
- Metadata (computing)
- Military
- MPEG-7
- Multimedia information retrieval
- Multimodal interaction
- Multiple-instance learning
- Nearest neighbor search
- Orthogonal transforms (Discrete Tchebichef moments)
- Precision and recall
- Query by example
- Relevance feedback
- Remote sensing
- Segmentation (image processing)
- Surveillance camera
- Texel (graphics)
- Wavelet transform
- SameAs
- BWaE
- CBIR
- Consulta d'imatges mitjançant exemple
- Consulta de imágenes mediante ejemplo
- Content Based Image Retrieval
- Content-based image retrieval
- m.037p0p
- Q1128648
- QBIC
- Recherche d'image par le contenu
- Sisältöpohjainen kuvatiedonhaku
- Αυτόματη Ανάκτηση Εικόνων
- Поиск изображений по содержанию
- Пошук зображень за вмістом
- بازیابی محتوامحور تصاویر
- 基于内容的图像检索
- SeeAlso
- Reverse image search
- Subject
- Category:Applications of artificial intelligence
- Category:Applications of computer vision
- Category:Image search
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