Searching for images from large repositories are tree some task. Rather than searching for image contents as whole intersting features of the images are looked for. We have used feature descriptors to compare images and similar images are grouped using classification algorithms. There by intersting images can be retrived effectively.
Shine Raj G : is working as Asst. Professor in Computer Science department. He has more than 8 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining.
Radhakrishnan B : is working as Asst. Professor in Computer Science department. He has more than 15 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, image mining and Artificial Intelligence. Also he is a member of CSI
Anver Muhammed K.M : is working as System Administrator in Computer Science department. He has more than 10 years experience in System Administration and Implementation. His research interests include image processing and network security.
CBIR, Object retrieval, Image representation,
Features Descriptors,BoW, Classification of Images
In this work, a CBIR system was implemented, combining the Scale Invariant Feature Transform (SIFT) descriptor and the Naive Bayes Nearest Neighbor (NBNN) algorithm in order to classify images according to their content. Due to the complexity and “subjectivity” of the problem, a simple classifier was applied. The advantage of this method is related to the absence of a learning phase. It just needs to assemble a group of features that label a class.
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