Google Image Visualization and Filtering
Yuli Gao,
Jianping Fan
Computer Science Department, UNC
Introduction:
Welcome to my Google Image demo page! This demo is mainly to demonstrate how machine leaning, image analysis and visualization techniques can work together to enhance content-based image retrieval and junk image filtering.
Installation:
This system is built as a Java applet, so the only thing you need to have JRE (1.6+ preferred) installed on your machine before you can run this demo. If you do not have a JRE, you can download it for free here: http://java.sun.com/javase/downloads/index.jsp
Usage:
Simply use your favorite Internet browser and logon to this URL: www.cs.uncc.edu/~jfan/google_demo/
At your first time of visit, the browser will try to download the applet. After the download is completed, you need to approve the “Security Warning” for the demo to work, because the applet needs to access network to download images from Google.
If everything goes ok, you should be able to see the applet. And now you are ready to use its functions as described below:
(a)
First, think of a keyword for
image search, and type in the top left text box.
(b)
Given the keyword, the system
will download a stream of images from Google image search engine using its
keyword-based search. By default, the system will download roughly 200 images
from Google, but the number of images to be downloaded can be controlled
through the 2nd text box at the top of the applet. If everything
goes fine, you should be able to see the layout of the search result. An
example is shown in Figure 1.
Notice that although downloading images is reasonably fast, computing the
layout is time-consuming. Therefore you will probably experience slow response
or may even run out of memory when the number of download images gets really
large (400 for my machine).
(c) The system projects the images based on their content similarities, so related images are clustered together while junk images are pushed out as outliers. Users can easily identify and navigate to a sub-region of the display without caring about other unrelated images.
Figure 1: the layout of the search result using the keyword “moon”.
Figure 2: User indicates two relevant images by selecting them (highlighted in red), the system then filter out irrelevant images based on this constraint and re-layout of the filtered search results.
(d)
Users can then dynamically
interact with the system with the following system functionalities:
a)
Search Result Exploration: Zoom (scroll middle button), Pan (drag mouse while pressing left button),
Rotate (drag mouse while pressing the
SHIFT + left button) or change the size of icons (scroll mouse middle button while pressing the SHIFT key)
b)
Image Acquisition: Obtain original
images (double-click on a specific image
and a new image window will popup) or link to the website where the image
is originally found (click on the popup
image window and it will lead you to the website).
c) Relevance Feedback for Image Filtering: Provide to the system a couple of positive examples to refine the return set and its layout. You can first CTRL-click on specific images to make selections of relevant images. After that, you can RIGHT-CLICK on anywhere inside the applet to fire a filtering operation. The system then takes your inputs as relevancy constraints and filter out irrelevant images (hopefully) before a new layout is computed. If the filtered result is not satisfactory, you can continue to select more relevant samples for filtering, or try to come up with new keywords to capture your targeting concept.
A filtering example is given in Figure 2, where most irrelevant images are filtered out by providing two relevant samples.
Contact Information:
This work is accomplished while I was a PhD student at UNC Charlotte. If you have encountered any difficulties, feel free to contact me via ygao@uncc.edu, or Dr. Jianping Fan via jfan@uncc.edu
Thanks and enjoy playing the demo.