Efficient parallel image clustering and search on a heterogeneous platform

We present a parallel image clustering and search framework for large scale datasets that does not require image annotation, segmentation or registration. This work addresses the image search problem while avoiding the need for user-specified or auto-generated metadata. Instead we rely on image data alone to avoid the ambiguity inherent in user-provided information. We propose a parallel algorithm exploiting heterogeneous hardware resources to generate global descriptors for the set of input images. Given a group of query images we derive the global descriptors in parallel. Secondly, we propose to build a customisable search tree of the image database by performing a hierarchical K-means (H-Kmeans) clustering of the corresponding descriptors. Lastly, we design a novel parallel vBFS algorithm to search through the H-Kmeans tree and locate the set of closest matches for query image descriptors. To validate our design we analyse the search performance and energy efficiency under a range of hardware clock frequencies and in comparison with alternative approaches. The result of our analysis shows that the framework greatly increases the search efficiency and thereby reduces the energy consumption per query.