Grid-based local feature bundling for efficient object search and localization

Abstract

We propose a new grid-based image representation for discriminative visual object search, with the goal to efficiently locate the query object in a large image collection. After extracting local invariant features, we partition the image into non-overlapping rectangular grid cells. Each grid bundles the local features within it and is characterized by a histogram of visual words. Given both positive and negative queries, each grid is assigned a mutual information score to match and locate the query object. This new image representation offers two great benefits for efficient object search: 1) as the grid bundles local features, the spatial contextual information enhances the discriminative matching; and 2) it enables faster object localization by searching visual object in the grid-level image. To evaluate our approach, we perform experiments on a very challenging logo database BelgaLogos [1] of 10,000 images. The comparison with the state-of-the-art methods highlights the effectiveness of our approach in both accuracy and speed.