Combining dense features with interest regions for efficient part-based image matching

One of the most popular approaches for object recognition is bag-of-words which represents an image as a histogram of the frequency of occurrence of visual words. But it has some disadvantages. Besides requiring computationally expensive geometric verification to compensate for the lack of spatial information in the representation, it is particularly unsuitable for sub-image retrieval problems because any noise, background clutter or other objects in vicinity influence the histogram representation. In our previous work, we addressed this issue by developing a novel part-based image matching framework that utilizes spatial layout of dense features within interest regions to vastly improve recognition rates for landmarks. In this paper, we improve upon the previously published recognition results by more than 12% and achieve significant reductions in computation time. A region of interest (ROI) selection strategy is proposed along with a new voting mechanism for ROIs. Also, inverse document frequency weighting is introduced in our image matching framework for both ROIs and dense features inside the ROIs. We provide experimental results for various vocabulary sizes on the benchmark Oxford 5K and INRIA Holidays datasets.