Can anyone suggest good algorithms for CBIR?

Question

Project: Content-Based Image Retrieval - Semi-supervised (manual tagging is done on images while training)

Description

I have 1000000 images in the database. The training is manual (supervised) - title and tags are provided for each image. Example: coke.jpg Title: Coke Tags: Coke, Can

Using the images and tags, I have to train the system. After training, when I give a new image (already in database/ completely new) the system should output the possible tags the image may belong to and display few images belonging to each tag. The system may also say no match found.

Questions:

1) What is mean by image fingerprint? What is the image fingerprint size expected? (important because there will be millions of images to be inserted in the database)

2) What is the field format of that fingerprint in the database? (important because a fast search is needed … the script should search in a 1M images database in less than 1 second)

3) What are the descriptors (algorithms) we use to analyze them?

Thanks in advance

Answer 1

Content-based image retrieval (CBIR) systems work by retrieving images that are related to the query image (QI) from huge databases. The convenient CBIR systems extract limited feature sets which confine the retrieval efficacy. In this work, extensive robust and important features were extracted from the database of the image and then stored in the feature repository. This feature set is composed of color signature with the shape and color of texture features, where features are similarly extracted from the given QI. 

Image fingerprint is a meaningful representation of the image. You can't use the single pixels of course. The most rational way to do it is to minimize the correlation between basis. In layman definition, if you take a 64x64 image probably the two pixels at the top left corner will be the same or similar. It's ineffective to use as input each single 64^2 pixels, you need something better. Try to have a look at what the Principal Component Analysis does.

And regarding the building an inverted index of your database, it's totally up to you. Extremism it, you could use a bit, that tells you whether the image is dark or not. Better, you do PCA on the image and experiment with different numbers of features (it's not always the case that more features are better).

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