Fast interactive information retrieval with sampling-based MDS on GPU architectures

Abstract

Relevance feedback algorithms improve content-based image retrieval (CBIR) systems by effectively using relevant/non-relevant images labeled by users. The main constraint of these algorithms is the update time for large datasets. Opening the graphics processing units (GPUs) to general purpose computation provides an opportunity for performing parallel computation on a powerful platform. In this paper, we suggest a fast interactive interface for CBIR which includes the conventional ranked list view along with two additional views based on fast k-means clustering and fast sampling-based multidimensional scaling (SBMDS) on a multi-core GPU architecture. We study the performance and efficiency of our framework on a collection of Maya syllabic glyph images. Experimental results show the improvement of retrieval performance at interactive speeds.