amatov / DifferentialDiagnosisCBIR

https://www.researchgate.net/publication/375238378_Differential_Diagnosis_of_Diseases_Associated_with_Protein_Aggregation_by_Machine_Learning_and_Analysis_of_Fundus_Fluorescence_in_Retinal_Images_2022_-_2023

bag-of-visual-words bag-of-bags-of-words computer-vision-python k-means-clustering surf-feature-extraction elastic-search reverse-image-search image-retrieval-query jaccard-similarity-estimation min-wise-independent-permutations canny-edge-detector sift-descriptors speeded-up-robust-features sift-keypoints surf-descriptor surf-detection content-based-image-retrieval locality-sensitive-hashing data-mining

Python code I wrote to implement a Reverse Image Search / Content-Based Image Retrieval (CBIR)

Based on the feature descriptors generated by the scale-invariant feature transform and the bag-of-words model (BoW), it performs reverse image search.

Content-based image retrieval query is accomplished via ElasticSearch using ranking based on a k-means clustering algorithm.

See my preliminary demo of the BoW ranking at: https://vimeo.com/365998906/e94e02a8af

For further information regarding differential diagnosis based on in vivo imaging, please see: https://github.com/amatov/FluorescentFundusNeurodegeneration