The technique of Content-based Image Retrieval (CBIR) takes a query image as the input and ranks images from a database of target images, producing the output. CBIR is an image to image search engine with a specific goal. A database of target images is required for retrieval. The target images with the minimum distance from the query image are returned. We can use the image directly for similarity, but the problems are as follows:
- The image is of huge dimensions
- There is a lot of redundancy in pixels
- A pixel doesn't carry the semantic information
So, we train a model for object classification and use the features from the model for retrieval. Then we pass the query image and database of targets through the same model to get the features. The models can also be called encoders as they encode the information about the images for the particular task. Encoders should be able to capture global and local features. We can use the models that we studied in the image classification...
