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 126–150 of 195 papers

TitleStatusHype
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
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
Knowledge Aware Semantic Concept Expansion for Image-Text Matching—0
Large-margin Learning of Compact Binary Image Encodings—0
Large Scale Deep Convolutional Neural Network Features Search with Lucene—0
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval—0
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling—0
Learning Hash Function through Codewords—0
Learning Non-Metric Visual Similarity for Image Retrieval—0
Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval—0
Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans—0
Lesion Search with Self-supervised Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified