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 176–195 of 195 papers

TitleStatusHype
Efficient Object Embedding for Spliced Image Retrieval—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium—0
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval—0
An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features—0
A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers—0
A new Local Radon Descriptor for Content-Based Image Search—0
An Improved Relevance Feedback in CBIR—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval—0
A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images—0
A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images—0
A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval—0
A Self-Balanced Min-Cut Algorithm for Image Clustering—0
A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation—0
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching—0
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval—0
A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning—0
Autoencoding the Retrieval Relevance of Medical Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified