PPT-Locality Sensitive Hashing and Large Scale Image Search

Yunchao Gong UNC Chapel Hill yunchaocsuncedu The problem Large scale image search We have a candidate image Want to search a large database to find similar images Search the internet. CSE P 576. Larry Zitnick (. larryz@microsoft.com. ). 20,000 images of Rome. =. ?. Large scale matching. How do we match millions or billions of images in under a second?. Is it even possible to store the information necessary?. Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. Management. 7. . course. Reminder. Disk. . and RAM. RAID . Levels. Disk. . space. management. Buffering. Heap. . files. Page. . formats. Record. . formats. Today. System . catalogue. Hash-based. Lecture 6: Locality Sensitive Hashing (LSH). Nearest Neighbor . Given a set P of n points in R. d. Nearest Neighbor . Want to build a data structure to answer nearest neighbor queries. Voronoi. Diagram. Abstract. Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. . However, how to protect customers’ confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud.. Josef . Sivic. http://. www.di.ens.fr. /~josef. INRIA, . WILLOW, ENS/INRIA/CNRS UMR 8548. Laboratoire. . d’Informatique. , . Ecole. . Normale. . Supérieure. , Paris. With slides from: O. Chum, K. . CST203-2 Database Management Systems. Lecture 7. Disadvantages. on index structure:. We must access an index structure to locate data, or must use binary search, and that results in more I/O operations. Consider a set of data with N data items stored in some data structure. We must be able to insert, delete & search for items. What are possible ways to do this? What is the complexity of each structure & method ?. Naifan Zhuang, Jun Ye, Kien A. Hua. Department of Computer Science. University of Central Florida. ICPR 2016. Presented by Naifan Zhuang. Motivation and Background. According to a report from Cisco, by 2019:. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. Hashing for Large-Scale Visual Search. Shih-Fu . Chang. www.ee.columbia.edu/dvmm. Columbia University. December 2012. Joint work with . Junfeng. He (Facebook), . Sanjiv. Kumar (Google), Wei Liu (IBM Research), and Jun Wang (IBM . CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. 3. William Cohen. 1. Outline. Randomized methods - so far. SGD with the hash trick. Bloom filters. count-min sketches. Today:. Review and discussion. More on count-min. Morris counters. locality sensitive hashing. Amjad. . Daoud. , Ph.D.. http://iswsa.acm.org/mphf. Practical Perfect Hashing for very large Key-Value Databases . Abstract. This presentation describes a practical algorithm for perfect hashing that is suitable for very large KV (key, value)...