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

 

 

Relevance Feedback

Image Retrieval with Feature Selection and Relevance Feedback

We propose a new content based image retrieval (CBIR) system combined with relevance feedback and the online feature selection procedures. A measure of inconsistency from relevance feedback is explicitly used as a new semantic criterion to guide the feature selection. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy. Experimental results show that the proposed method obtains higher retrieval accuracy than a commonly used approach.

Long-Term Cross-Session Relevance Feedback Using Virtual Features

We propose a novel RF framework, which facilitates the combination of short-term and long-term learning processes by integrating the traditional methods with a new technique called the virtual feature. The feedback history with all the users is digested by the system and is represented in a very efficient form as a virtual feature of the images. By monitoring the changes in retrieval performance, the proposed system can automatically adapt the concepts according to the new subject concepts. The results manifest that the proposed framework outperforms the traditional within-session and log-based long-term RF techniques.

Integrating Relevance Feedback Techniques for Image Retrieval

We propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques in a content-based image retrieval system. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone. Further, the storage demand is significantly reduced by the concept digesting technique.

Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

Most of the current image retrieval systems use “one-shot” queries to a database to retrieve similar images. Typically a K-nearest neighbor kind of algorithm is used, where weights measuring feature importance along each input dimension remain fixed (or manually tweaked by the user), in the computation of a given similarity metric. In this paper, we present a novel probabilistic method that enables image retrieval procedures to automatically capture feature relevance based on user's feedback and that is highly adaptive to query locations. Experimental results are presented that demonstrate the efficacy of our technique using both simulated and real-world data.

Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

Most of the image retrieval systems at the time used “one-shot” queries to a database to retrieve a similar image and typically a K-nearest neighbor kind of algorithm was used, where weights measuring feature importance along each input dimension remained fixed (or manually tweaked by the user), in the computation of a given similarity metric. However, the similarity did not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights was time consuming and exhausting and required a very sophisticated user. A novel probabilistic method that enables image retrieval procedures to automatically capture feature relevance based on user’s feedback and that is highly adaptive to query locations is presented. Our findings demonstrated the efficacy of our technique using both simulated and real-world data.