Image Similarity Search
12 papers with code • 0 benchmarks • 1 datasets
Image credit: The 2021 Image Similarity Dataset and Challenge
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Similarity search approaches based on graph walks have recently attained outstanding speed-accuracy trade-offs, taking aside the memory requirements.
The search can directly warn fake news posters and online users (e. g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media.
It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment.
As a consequence, such features are powerful to compare semantically related images but not really efficient to compare images visually similar but semantically unrelated.
They have overlooked the wide characteristic changes of different classes and can not model abundant intra-class variations for generations.
These proxy representations are thus used to construct a global index that encompasses the similarities between all places in the dataset, allowing for highly informative mini-batch sampling at each training iteration.
It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in natural images and a two-stage method for efficiently searching binary hash codes using Elasticsearch (ES).
This paper presents a new approach to image similarity search in the context of fashion, a domain with inherent ambiguity due to the multiple ways in which images can be considered similar.