Object Cataloging Using Heterogeneous Local Features for Image Retrieval
Mohammad Khairul Islam, Farah Jahan, Joong-Hwan Baek · KSII Transactions on Internet and Information Systems · 2015
We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval.Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem.In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries.In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently.A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary.The proposed method demonstrates its excellence in classifying objects in various complex backgrounds.Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.