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 31–40 of 195 papers

TitleStatusHype
Accurate and Fast Pixel Retrieval with Spatial and Uncertainty Aware Hypergraph Diffusion—0
A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning—0
Efficient Object Embedding for Spliced Image Retrieval—0
On Validation of Search & Retrieval of Tissue Images in Digital Pathology—0
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval—0
Autoencoding the Retrieval Relevance of Medical Images—0
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching—0
A Hybrid Approach for Improved Content-based Image Retrieval using Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified