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 51–60 of 195 papers

TitleStatusHype
Class Anchor Margin Loss for Content-Based Image Retrieval—0
Classifying magnetic resonance image modalities with convolutional neural networks—0
Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval—0
Color Image Retrieval Using Fuzzy Measure Hamming and S-Tree—0
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives—0
Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval—0
Computing Similarity between Cultural Heritage Items using Multimodal Features—0
A Hybrid Approach for Improved Content-based Image Retrieval using Segmentation—0
Constrained Mass Optimal Transport—0
A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified