SOTAVerified

Content-Based Image Retrieval

Content-Based Image Retrieval is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.

Source: Camera Obscurer: Generative Art for Design Inspiration

Papers

Showing 4150 of 195 papers

TitleStatusHype
BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding0
Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders0
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching0
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey0
Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval0
CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis0
A new Local Radon Descriptor for Content-Based Image Search0
Challenging deep image descriptors for retrieval in heterogeneous iconographic collections0
An Improved Relevance Feedback in CBIR0
A Hybrid Approach for Improved Content-based Image Retrieval using Segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94Unverified