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 171–180 of 195 papers

TitleStatusHype
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
A Fast Content-Based Image Retrieval Method Using Deep Visual Features—0
Aggregating Binary Local Descriptors for Image Retrieval—0
A Hybrid Approach for Improved Content-based Image Retrieval using Segmentation—0
Efficient Object Embedding for Spliced Image Retrieval—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium—0
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval—0
An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified