A novel framework for fast scene matching in consumer image collections

The widespread utilization of digital visual media has motivated many research efforts towards efficient search and retrieval from large photo collections. Traditionally, SIFT feature-based methods have been widely used for matching photos taken at particular locations or places of interest. These methods are very time-consuming due to the complexity of the features and the large number of images typically contained in the image database being searched. In this paper, we propose a fast approach to matching images captured at particular locations or places of interest by selecting representative images from an image collection that have the best chance of being successfully matched by using SIFT, and relying on only these representative images for efficient scene matching. We present a unified framework incorporating a set of discriminative features that can effectively select the images containing signature elements of particular locations from a large number of images. The proposed approach produces an order of magnitude improvement in computational time for matching similar scenes in an image collection using SIFT features. The experimental results demonstrate the efficiency of our approach compared to the traditional SIFT, PCA-SIFT, and SURF-based approaches.