Skip to content

Find similar images in dataset for a new image using Resnet50+KNN. In order to solve curse of dimensionality using PCA to reduce the dimensionality of features.

Notifications You must be signed in to change notification settings

popCain/ImageSimilarity

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

17 Commits
 
 
 
 
 
 
 
 

Repository files navigation

ImageSimilarity

Find similar images in dataset for a new image using Resnet50+KNN. In order to solve curse of dimensionality using PCA to reduce the dimensionality of features.

Resnet50(trained on imageNet)

  1. Create Resnet50 from keras without toplayer to get convolutional features(2048 dimension) as output instead of the image classification probability.
  2. Extract the convolutional features for every images of dataset( Feature_size:[number_images, 2048] ).

KNN(Not a classification issue)

  1. Fit the nearest neighbor algorithm to the extracted features from dataset
  2. Extract convolutional feature of test image(new), and calculate the distance(image_Similarity) between the test image and each image of dataset.

PCA

  1. Apply PCA to extracted features and reduce the dimensions.
  2. Fit the nearest neighbor algorithm to the new features

About

Find similar images in dataset for a new image using Resnet50+KNN. In order to solve curse of dimensionality using PCA to reduce the dimensionality of features.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published