I have a database of images that contains identity cards, bills and passports.
I want to classify these images into different groups (i.e identity cards, bills and passports).
As I read about that, one of the ways to do this task is clustering (since it is going to be unsupervised).
The idea for me is like this: the clustering will be based on the similarity between images (i.e images that have similar features will be grouped together).
I know also that this process can be done by using k-means.
So the problem for me is about features and using images with K-means.
If anyone has done this before, or has a clue about it, please would you recommend some links to start with or suggest any features that can be helpful.
7
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3 Answers
5
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Most simple way to get good results will be to break down the problem into two parts :
- Getting the features from the images: Using the raw pixels as features will give you poor results. Pass the images through a pre trained CNN(you can get several of those online). Then use the last CNN layer(just before the fully connected) as the image features.
- Clustering of features : Having got the rich features for each image, you can do clustering on these(like K-means).
I would recommend implementing(using already implemented) 1, 2 in Keras and Sklearn respectively.
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Label a few examples, and use classification.
Clustering is as likely to give you the clusters "images with a blueish tint", "grayscale scans" and "warm color temperature". That is a quote reasonable way to cluster such images.
Furthermore, k-means is very sensitive to outliers. And you probably have some in there.
Since you want your clusters correspond to certain human concepts, classification is what you need to use.
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I have implemented Unsupervised Clustering based on Image Similarity using Agglomerative Hierarchical Clustering.
My use case had images of People, so I had extracted the Face Embedding (aka Feature) Vector from each image. I have used dlib for face embedding and so each feature vector was 128d.
In general, the feature vector of each image can be extracted. A pre-trained VGG or CNN network, with its final classification layer removed; can be used for feature extraction.
A dictionary with KEY as the IMAGE_FILENAME and VALUE as the FEATURE_VECTOR can be created for all the images in the folder. This will make the co-relation between the filename and it’s feature vector easier.
Then create a single feature vector say X, which comprises of individual feature vectors of each image in the folder/group which needs to be clustered.
In my use case, X had the dimension as : NUMBER OF IMAGE IN THE FOLDER, 128 (i.e SIZE OF EACH FEATURE VECTOR). For instance, Shape of X : 50,128
This feature vector can then be used to fit an Agglomerative Hierarchical Cluster. One needs to fine tune the distance threshold parameter empirically.
Finally, we can write a code to identify which IMAGE_FILENAME belongs to which cluster.
In my case, there were about 50 images per folder so this was a manageable solution. This approach was able to group image of a single person into a single clusters. For example, 15 images of PERSON1 belongs to CLUSTER 0, 10 images of PERSON2 belongs to CLUSTER 2 and so on…
