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 131–140 of 195 papers

TitleStatusHype
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
Image Retrieval And Classification Using Local Feature Vectors—0
Image Retrieval Based on LBP Pyramidal Multiresolution using Reversible Watermarking—0
Image Retrieval System Base on EMD Similarity Measure and S-Tree—0
Image Retrieval using Histogram Factorization and Contextual Similarity Learning—0
Image Retrieval with a Bayesian Model of Relevance Feedback—0
Kernelized Deep Convolutional Neural Network for Describing Complex Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified