Discovery of Eight z ~ 6 Quasars in the Sloan Digital Sky Survey Overlap Regions

We present the discovery of eight quasars at z~6 identified in the Sloan Digital Sky Survey (SDSS) overlap regions. Individual SDSS imaging runs have some overlap with each other, leading to repeat observations over an area spanning >4000 deg^2 (more than 1/4 of the total footprint). These overlap regions provide a unique dataset that allows us to select high-redshift quasars more than 0.5 mag fainter in the z band than those found with the SDSS single-epoch data. Our quasar candidates were first selected as i-band dropout objects in the SDSS imaging database. We then carried out a series of follow-up observations in the optical and near-IR to improve photometry, remove contaminants, and identify quasars. The eight quasars reported here were discovered in a pilot study utilizing the overlap regions at high galactic latitude (|b|>30 deg). These quasars span a redshift range of 5.86<z<6.06 and a flux range of 19.3<z_AB<20.6 mag. Five of them are fainter than z_AB=20 mag, the typical magnitude limit of z~6 quasars used for the SDSS single-epoch images. In addition, we recover eight previously known quasars at z~6 that are located in the overlap regions. These results validate our procedure for selecting quasar candidates from the overlap regions and confirming them with follow-up observations, and provide guidance to a future systematic survey over all SDSS imaging regions with repeat observations.Comment: AJ in press (8 pages

Evolution of a Web-Scale Near Duplicate Image Detection System

Detecting near duplicate images is fundamental to the content ecosystem of photo sharing web applications. However, such a task is challenging when involving a web-scale image corpus containing billions of images. In this paper, we present an efficient system for detecting near duplicate images across 8 billion images. Our system consists of three stages: candidate generation, candidate selection, and clustering. We also demonstrate that this system can be used to greatly improve the quality of recommendations and search results across a number of real-world applications. In addition, we include the evolution of the system over the course of six years, bringing out experiences and lessons on how new systems are designed to accommodate organic content growth as well as the latest technology. Finally, we are releasing a human-labeled dataset of ~53,000 pairs of images introduced in this paper

Visual Search at eBay

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. A demonstration video can be found at https://youtu.be/iYtjs32vh4