Image Classification with Bag of SIFT Representation + SVM Classifer

[ramakanth98]

1. Given a dataset of images, we need to represent them using the bag of SIFT representation. This involves clustering SIFT descriptors into a visual word vocabulary, counting the frequency of descriptors in each cluster, and generating histograms of visual words as image representations. The goal is to efficiently represent images while retaining important visual information for use in computer vision tasks.

2. Given a 10-way image classification problem, we need to train 10 binary SVM classifiers using one-vs-all approach to classify test images. During testing, the classifier with the most confidently positive result is selected. The goal is to accurately classify test images using optimized C values for regularization strength.

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[prathimacode-hub]

Hello @ramakanth98, you can create this issue in other repository here. This topic doesn't belong here.