Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Databases

A software system for automated identification and retrieval of moth images based on wing attributes

Described is the development of an automated moth species identification and retrieval system (SPIR) using computer vision and pattern recognition techniques. The core of the system was a probabilistic model that infered Semantically Related Visual (SRV) attributes from low-level visual features of moth images in the training set, where moth wings were segmented into information-rich patches from which the local features were extracted, and the SRV attributes were provided by human experts as ground-truth. For the large amount of unlabeled test images in the database or added into the database later on, an automated identification process was evoked to translate the detected salient regions of low-level visual features on the moth wings into meaningful semantic SRV attributes. We further proposed a novel network analysis based approach to explore and utilize the co-occurrence patterns of SRV attributes as contextual cues to improve individual attribute detection accuracy. Working with a small set of labeled training images, the approach constructed a network with nodes representing the SRV attributes and weighted edges denoting the co-occurrence correlation.