Similarity space projection for web image search and annotation

Abstract

image search has been explored and developed in academic as well as commercial areas for over a decade. To measure the between and user queries, most of the existing image search systems try to convert an image to textual keywords by analyzing the textual information available (such as surrounding text and image filename) with or without leveraging image visual features (such as color, texture, shape). In this way, the existing systems transform Web images to the (text) so as to compare the relevance of to the query. In this paper, we present a novel solution to image search - projection (SSP). This algorithm takes and queries as two heterogeneous object peers, and projects them into a third Euclidean similarity space in which their can be directly measured. The rule of projection guarantees that in the new the relevant are kept close to the corresponding query and those irrelevant ones are away from it. Experiments on real-world image collections showed that the proposed algorithm significantly outperformed traditional information retrieval models (such as vector model) in the application of image search. Besides image search, we demonstrate that this algorithm can also be applied to image annotation scenario, and has promising performance. Thus, this algorithm unifies image search and image annotation into same framework.