Spatial Aggregation of Visual Features for Image Data Search in a Large Geo-Tagged Image Dataset

Two main requirements of searching in a big image database are performance and accuracy. For an accurate similarity search, high-dimensional visual features are preferred while low-dimensional features resulted from dimension reduction techniques are utilized in index structures for performance. Most state-of-the-art indexes utilize low-dimensional visual descriptors to avoid the computing overhead of high-dimensionality in image search, which sacrifices search accuracy. We propose a new descriptor that balances the trade-off between accuracy and performance in image search by extending the representation of an image with the feature set of multiple similar images located in its vicinity (referred to as Spatially-Aggregated Visual Feature Descriptor (SVD)). SVD potentially preserves the visual features of images in both high and low-dimensional spaces better than conventional visual descriptors. Through an empirical evaluation on big datasets, indexing images using SVD provided a significant improvement in search accuracy comparing to using conventional descriptors while maintaining the same performance.