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 121–130 of 195 papers

TitleStatusHype
Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram—0
Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging—0
Multimorbidity Content-Based Medical Image Retrieval Using Proxies—0
MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus—0
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval—0
Neuromorphic Computing for Content-based Image Retrieval—0
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation—0
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function—0
Pareto-depth for Multiple-query Image Retrieval—0
PinView: Implicit Feedback in Content-Based Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified