Image similarity search using deep learning

This project demonstrates, how we can make use of deep learning to do state-of-the-art image similarity search. I have used tensorflow and some publicly available datasets.

Results

How to run

1. Download imagenet folder, extraxt and keep it in server directory
2. Download datasets for footwares, apparels keep them inside a directory under upload folder. Final folder strcture will be as below

      root folder  
      │
      └───lib
      │   
      └───server
      |   │───rest-server.py
      |   │───imagenet
      |   │───static
      |   │───templates
      |   │───uploads
      |        │────dogs_and_cats
      |              │────shoes
      |              │────sandals
      |              │────slippers
      |              │────boots
      |              │────apparels

3. Run image vectorizer which passes each data through an inception-v3 model and collects the bottleneck layer vectors and stores in disc. Edit dataset paths accordingly indide the image_vectorizer.py

  python server/image_vectorizer.py 

This will generate two files namely, image_list.pickle and saved_features.txt. Keep them inside lib folder where search.py script is available.

4. Start the server by running rest-server.py. This project uses flask based REST implementation for UI

  python server/rest-server.py 

5. Once the server starts up, access the url 127.0.0.1:5000 to get the UI. Now upload any file and see 9 similar images. You can change the value of K from 9 to any values, but dont foreget to update the html file accordingly for displaying.

One interesting application of this project is a recommendation engine based on image features.Here is an example of similar project of mine. Here instead of a web UI i have used an android UI. Once the user clicks a product image, the image will go to the server and k-number of similar product images can be displayed on UI as product recommendations. Theses rescommendations are purely based on image similarity. This kind of recommendations have high potentials in fashion-based ecommerce industry.