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 171–180 of 195 papers

TitleStatusHype
Color Image Retrieval Using Fuzzy Measure Hamming and S-Tree—0
Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging—0
RBIR using Interest Regions and Binary Signatures—0
Fast Dictionary Matching for Content-based Image Retrieval—0
Exploring EEG for Object Detection and Retrieval—0
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and RetrievalCode0
Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram—0
Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey—0
A Hybrid Approach for Improved Content-based Image Retrieval using Segmentation—0
Texture Retrieval via the Scattering Transform—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified