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

• Main Authors: Jiang, Yuning, Meng, Jingjing, Yuan, Junsong
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• Online Access: https://hdl.handle.net/10356/100403; http://hdl.handle.net/10220/18118
• Institution: Nanyang Technological University

We propose a new grid-based image representation for dis- criminative visual object search, with the goal to efficiently locate the query object in a large image collection. After ex- tracting local invariant features, we partition the image into non-overlapped 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 lo- cate the query object. This new image representation brings in two great benefits for efficient object search: 1) as the grid bundles local features, the spatial contextual information en- hances the discriminative matching; and 2) it enables faster object localization by searching visual object on 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.