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

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Showing 41–50 of 195 papers

TitleStatusHype
A Comparative Analysis of Retrieval Techniques In Content Based Image Retrieval—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval—0
An Improved Relevance Feedback in CBIR—0
Challenging deep image descriptors for retrieval in heterogeneous iconographic collections—0
A new Local Radon Descriptor for Content-Based Image Search—0
A Decade Survey of Content Based Image Retrieval using Deep Learning—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—0
A Bag of Visual Words Model for Medical Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified