On very large scale test collection for landmark image search benchmarking

High quality test collections have been becoming more and more important for the technological advancement in geo-referenced image retrieval and analytics. In this paper, we present a large scale test collection to support robust performance evaluation of landmark image search and corresponding construction methodology. Using the approach, we develop a very large scale test collection consisting of three key components: (1) 355,141 images of 128 landmarks in five cities across three continents crawled from Flickr; (2) different kinds of textual features for each image, including surrounding text (e.g. tags), contextual data (e.g. geo-location and upload time), and metadata (e.g. uploader and EXIF); and (3) six types of low-level visual features. In order to support robust and effective performance assessment, a series of baseline experimental studies have been conducted on the search performance over both textual and visual queries. The results demonstrate importance and effectiveness of the test collection. HighlightsA comprehensive methodology and procedure are developed to construct very large landmark search test collection.A large scale benchmarking test collection has been developed for effective, reliable and robust evaluation and comparisons of landmark image search systems.Extensive experiments have been conducted using CBIR and TBIR methods to provide baseline results and insights for advanced system development.