# Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation

arXiv: https://arxiv.org/abs/1409.5400 · HTML: https://mlsanity.com/paper/1409.5400/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2014.

## Built on by (most cited first, 3 of 3)

- [Which country is this picture from? New data and methods for DNN-based country recognition](https://mlsanity.com/paper/2209.02429/) (2022)
- [Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information](https://mlsanity.com/paper/2206.01326/) (2022)
- [Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks](https://mlsanity.com/paper/2107.14070/) (2021)

## Abstract (arXiv)

The task of a visual landmark recognition system is to identify photographed buildings or objects in query photos and to provide the user with relevant information on them. With their increasing coverage of the world's landmark buildings and objects, Internet photo collections are now being used as a source for building such systems in a fully automatic fashion. This process typically consists of three steps: clustering large amounts of images by the objects they depict; determining object names from user-provided tags; and building a robust, compact, and efficient recognition index. To this date, however, there is little empirical information on how well current approaches for those steps perform in a large-scale open-set mining and recognition task. Furthermore, there is little empirical information on how recognition performance varies for different types of landmark objects and where there is still potential for improvement. With this paper, we intend to fill these gaps. Using a dataset of 500k images from Paris, we analyze each component of the landmark recognition pipeline in order to answer the following questions: How many and what kinds of objects can be discovered automatically? How can we best use the resulting image clusters to recognize the object in a query? How can the object be efficiently represented in memory for recognition? How reliably can semantic information be extracted? And finally: What are the limiting factors in the resulting pipeline from query to semantics? We evaluate how different choices of methods and parameters for the individual pipeline steps affect overall system performance and examine their effects for different query categories such as buildings, paintings or sculptures.