Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2014

Abstract

We address the problem of visual instance mining, which is to extract frequently appearing visual instances automatically from a multimedia collection. We propose a scalable mining method by exploiting Thread of Features (ToF). Specifically, ToF, a compact representation that links consistent features across images, is extracted to reduce noises, discover patterns, and speed up processing. Various instances, especially small ones, can be discovered by exploiting correlated ToFs. Our approach is significantly more effective than other methods in mining small instances. At the same time, it is also more efficient by requiring much fewer hash tables. We compared with several state-of-the-art methods on two fully annotated datasets: MQA and Oxford, showing large performance gain in mining (especially small) visual instances. We also run our method on another Flickr dataset with one million images for scalability test. Two applications, instance search and multimedia summarization, are developed from the novel perspective of instance mining, showing great potential of our method in multimedia analysis.

Keywords

Clustering, Instance mining, Min-hash, Summarization, Thread of Features

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 22nd ACM international conference on Multimedia, MM 2014, Orlando, Florida, November 3-7

First Page

297

Last Page

306

ISBN

9781450330633

Identifier

10.1145/2647868.2654942

Publisher

ACM

City or Country

Orlando

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