SVM AND HOG BASED SCULPTURE RECOGNITION

Abstract

Historical monuments and their sculptures hold the story of the past attached to the monument. They give an idea of how the ancient people, the kings etc lived. We see a lot of ancient temples, tombs and other monuments. They have sculptures and other architectural designs on them to signify the era to which they belong. We need to preserve and pass these to our upcoming generations so that they can understand and can contribute their part in maintaining the heritage. Sculptures found in these monuments are also very helpful in understanding the culture. It has been observed that the ancient people (in India, for e.g.) many times worship the same god, but with a different name. If using a classifier we can know the name of the god or sculpture in the language of our choice, and then it is much likely that we’ll extract other useful information related to them too. In out proposed work we have worked on to build a classifier based on the features of sculptures (of god/goddesses) to classify the images of gods and goddesses. The images have been taken from various sources such as temples, forts, ancient monuments, sculptures and other images from INTERNET. The sculptures classification can prove useful as it can help us classify the god (i.e. label them as god A or god B), extract information about the god/goddess, the era or history of that particular sculpture. This is a thoughtful step towards preserving the rich history India has and make future generations learn and remember the past efficiently. The proposed method also helps in solving a 2-class classification problem which works on the basis of training the classifier with the HOG features of the images. The proposed method classifies the query image (of God/Goddesses) as belonging to either of the 4 classes on which it has been trained. We have taken 3 SVM classifiers here and used them in nesting with each other. The accuracy lies between 56 to 70%. The proposed method can be extended for information retrieval after classification.