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A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
Class labeling, many local descriptors, like SIFT and aKaZE, and local feature agreators, like Fisher Vector (FV). 54,000 Images and .mat files Fine-grain classification 2017 [184] O. Taran and S. Rezaeifar, et al. Stanford Dogs Dataset Images of 120 breeds of dogs from around the world. Train/test splits and ImageNet annotations provided. 20,580
An interface schema is created as part of the logical index container that provides the API hooks used to return search results with additional features integrated into Azure Search. Azure Search provides two different indexing engines: Microsofts own proprietary natural language processing technology or Apache Lucene analyzers. [3]
Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. [2] Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail ...
General scheme of 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 [1] for a scientific overview of the CBIR field).
Natural language processing, machine comprehension 2013 [87] [88] M. Richardson et al. The Penn Treebank Project Naturally occurring text annotated for linguistic structure. Text is parsed into semantic trees. ~ 1M words Text Natural language processing, summarization 1995 [89] [90] M. Marcus et al. DEXTER Dataset
Probabilistic latent semantic analysis (pLSA) [8] [9] and latent Dirichlet allocation (LDA) [10] are two popular topic models from text domains to tackle the similar multiple "theme" problem. Take LDA for an example. To model natural scene images using LDA, an analogy is made with document analysis: the image category is mapped to the document ...
Multimedia documents search engines and classifiers. Digital library: visual descriptors allow a very detailed and concrete search of any video or image by means of different search parameters. For instance, the search of films where a known actor appears, the search of videos containing the Everest mountain, etc. Personalized electronic news ...