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 91–100 of 195 papers

TitleStatusHype
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
Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval—0
Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection—0
iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice Embeddings—0
Image Annotation with ISO-Space: Distinguishing Content from Structure—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified