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
Evidential Transformers for Improved Image Retrieval—0
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval—0
Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning—0
Exploring EEG for Object Detection and Retrieval—0
Fast Dictionary Matching for Content-based Image Retrieval—0
Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification—0
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm—0
Further results on dissimilarity spaces for hyperspectral images RF-CBIR—0
Gabor Barcodes for Medical Image Retrieval—0
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified