Retrieve Similar Images

I am trying to build a similar image retrieval system where given an image, the system is able to show top ‘k’ most similar images to it. For this particular example, I am using the DeepFashion dataset where given an image containing say a shirt, you show top 5 clothes most similar to a shirt. A subset of this has 289,222 diverse clothes images in it. Each image is of shape: (300, 300, 3).

The approach I have includes:

  1. Train an autoencoder
  2. Feed each image in the dataset through the encoder to get a reduced n-dimensional latent space representation. For example, it can be 100-d latent space representation
  3. Create a table of shape m x (n + 2) where ‘m’ is the number of images and each image is compressed to n-dimensions. One of the column is the image name and the other column is a path to where the image is stored on your local system
  4. Given a new image, you feed it through the encoder to get the n-dimensional latent space representation
  5. Use something like cosine similarity, etc to compare the n-d latent space for new image with the table m x (n + 2) obtained in step 3 to find/retrieve top k closest clothes

How do I create the table mentioned in step 3?

I am planning on using TensorFlow 2.5 with Python 3.8 and the code for getting an image generator is as follows:

image_generator = ImageDataGenerator( rescale = 1./255, rotation_range = 135) train_data_gen = image_generator.flow_from_directory( directory = train_dir, batch_size = batch_size, shuffle = False, target_size = (IMG_HEIGHT, IMG_WIDTH), class_mode = 'sparse' 

How can get image name and path to image to create the m x (n + 2) table in step 3?

Also, is there any other better way that I am missing out on?


submitted by /u/grid_world
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