Mobile Image Retrieval Using Integration of Geo-sensing and Visual Descriptor

In this paper, we propose a new efficient photo image retrieval method that automatically indexed for searching relevant photo images using a combination of geo-referenced attributes and low-level visual features. Photo image is labeled with its GPS (Global Positioning System) coordinates at the moment of capture, and a flat layer index is generated with the pair of latitude and longitude. These are then utilized to create a hierarchical layer indexes for spatial information after uploaded to media server. Then, low-level visual features such as color histogram and edge histogram are extracted and combined with geo-spatial information for indexing and searching photo images. For user's querying process, the proposed method adopts two different steps as progressive approach, filtering and/or selecting the relevant subset prior to content-based retrieval. To evaluate the performance of the proposed descriptor, we assess the simulation performance in terms of average precision and F-score using digital photo collections. Comparing the proposed approach to search using visual content alone, an improvement of around 20% was observed in experimental trials. These results reveal that combination of context and content analysis is markedly more effective and meaningful than using only visual content for this task.