Unsupervised Object Discovery from Images by Mining Local Features Using Hashing

Authors:
Gibran Fuentes Pineda;Hisashi Koga;Toshinori Watanabe
Affiliations:
Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan 182-8585;Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan 182-8585;Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan 182-8585
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

Citing 4
Cited 0

Approximate nearest neighbors: towards removing the curse of dimensionality

STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Generic Object Recognition with Boosting

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Component-based Framework for Face Detection and Identification

International Journal of Computer Vision
Efficient Learning of Relational Object Class Models

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a new methodology for efficiently discovering objects from images without supervision. The basic idea is to search for frequent patterns of closely located features in a set of images and consider a frequent pattern as a meaningful object class. We develop a system for discovering objects from segmented images. This system is implemented by hashing only. We present experimental results to demonstrate the robustness and applicability of our approach.