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 81–90 of 195 papers

TitleStatusHype
Deep Features for CBIR with Scarce Data using Hebbian Learning—0
Deep Learning for Instance Retrieval: A Survey—0
Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents—0
Deep Learning Based Image Retrieval in the JPEG Compressed Domain—0
Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval—0
Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey—0
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified