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 61–70 of 195 papers

TitleStatusHype
Content Based Image Indexing and Retrieval—0
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era—0
CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis—0
Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval—0
A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers—0
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey—0
BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval—0
An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features—0
Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders—0
BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified