Linear Discriminant Analysis for Zero-shot Learning Image Retrieval

This paper introduces a new distance function for comparing images in the context of content-based image retrieval. Given a query and a large dataset to be searched, the system has to provide the user – as efficiently as possible – with a list of images ranked according to their distance to the query. Because of computational issues, traditional image search systems are generally based on conventional distance function such as the Euclidian distance or the dot product, avoiding the use of any training data nor expensive online metric learning algorithms. The drawback is that, in this case, the system can hardly cope with the variability of image contents. This paper proposes a simple yet efficient zero-shot learning algorithm that can learn a query-adapted distance function from a single image (the query) or from a few images (e.g. some user-selected images in a relevance feedback iteration), hence improving the quality of the retrieved images. This allows our system to work with any object categories without requiring any training data, and is hence more applicable in real world use cases. More interestingly, our system can learn the metric on the fly, at almost no cost, and the cost of the ranking function is as low as the dot product distance. By allowing the system to learn to rank the images, significantly and consistently improved results (over the conventional approaches) have been observed on the Oxford5k, Paris6k and Holiday1k datasets.