In part 1 of this series a definition of CBIR has been given. Let's now understand what's the typical flow for it. The diagram in figure 1 shows that there are two parts, one that happens offline and another which is online:
Starting from an image storage, a preliminary trained Deep Neural Network is used to extract the features from images. Extracted features are then stored in a feature database. This happens offline any time new images are added to the storage. What happens online is the search process itself. Any time a user uploads a query image, the same Deep Neural Network used for feature extraction is used to extract the input image features. Then the distance from the query image features and the features in the database are computed. The closer the distance, the higher the relevance is. The closest features are then sorted and the corresponding images are returned as results.
The data set used for this example is the Food 101 data set. It contains images of food recipes from international cuisine classified in 101 categories (classes). Its size is about 5 GB and contains 1000 images for each class. The structure of the ZIP archive includes:
Unfortunately there are no separate directories for training and test images: you have to split them by yourself (a sample Python script would be provided in GitHub later on). Once the train/test split has been done, the training of the Deep Neural Network (a CNN) could start.
The next post will walk through the details of the feature extraction.
Figure 1
Basic implementation: data set preparation
To make things more clear, let's start a first simple implementation using a supervised Deep Learning approach for feature extraction.The data set used for this example is the Food 101 data set. It contains images of food recipes from international cuisine classified in 101 categories (classes). Its size is about 5 GB and contains 1000 images for each class. The structure of the ZIP archive includes:
- an image directory, which contains all the images grouped into sub-directories, one for each food class;
- a meta directory, which contains four text files:
- classes.txt: the list of class names;
- labels.txt: a list of verification labels;
- train.txt: the list of the training images;
- test.txt: the list of the test images.
Figure 2
Unfortunately there are no separate directories for training and test images: you have to split them by yourself (a sample Python script would be provided in GitHub later on). Once the train/test split has been done, the training of the Deep Neural Network (a CNN) could start.
The next post will walk through the details of the feature extraction.
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