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 111–120 of 195 papers

TitleStatusHype
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified