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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 195 papers

TitleStatusHype
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval—0
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning—0
Radiological images and machine learning: trends, perspectives, and prospects—0
Unsupervised Multi-modal Hashing for Cross-modal retrieval—0
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval—0
Learning Hash Function through Codewords—0
Content-based image retrieval system with most relevant features among wavelet and color features—0
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image RetrievalCode0
Medical Image Super-Resolution Using a Generative Adversarial Network—0
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image RetrievalCode0
Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval—0
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning—0
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval—0
Representing pictures with emotions—0
An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features—0
Classification is a Strong Baseline for Deep Metric LearningCode0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Semantic bottleneck for computer vision tasks—0
Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features—0
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling—0
Content Based Image Retrieval from AWiFS Images Repository of IRS Resourcesat-2 Satellite Based on Water Bodies and Burnt Areas—0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval—0
Towards Practical Visual Search Engine within Elasticsearch—0
Classifying magnetic resonance image modalities with convolutional neural networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified