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 141–150 of 195 papers

TitleStatusHype
Scalable Image Retrieval by Sparse Product Quantization—0
Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap and Search Application—0
Semantic bottleneck for computer vision tasks—0
Semi-supervised lung nodule retrieval—0
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning—0
Stacked Autoencoders for Medical Image Search—0
Survey on Sparse Coded Features for Content Based Face Image Retrieval—0
Techniques for effective and efficient fire detection from social media images—0
Texture and Color-based Image Retrieval Using the Local Extrema Features and Riemannian Distance—0
Texture image retrieval using a classification and contourlet-based features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified