| Dataset 3 LHI_UCLA_Aerial_Image_5 |
| Contents and Statistics : |
| Category |
Number of image |
Free |
Total |
| School |
20 |
68 |
| Residential |
20 |
205 |
| Parking |
20 |
3495 |
| Marina |
20 |
203 |
| Intersection |
20 |
70 |
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Matlab codes to load this category (see MatlabToolbox page for more information):
clear; close all;
HOMEIMAGES = 'C:\Yourdatabase\LHI_UCLA_Aerial_Image_5\Images';
HOMEANNOTATIONS = 'C:\Yourdatabase\LHI_UCLA_Aerial_Image_5\Annotations';
HOMELABELMAPS = 'C:\Yourdatabase\LHI_UCLA_Aerial_Image_5\LabelMaps';
Code to load the database and mapping the names
rawDB = LHIdatabase(HOMEANNOTATIONS); NEWHOMELABELMAPS = 'C:/temp/yourposition/';D=LHIregulatenames(rawDB,HOMELABELMAPS,NEWHOMELABELMAPS,'aerial.txt'); |
 Figure1 Name table of all categories and correspond color of in labelmaps. Left three columns are R,G,B values respectively.
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Statistics of the category
General statistics including number of keypoints on the boundary for each object, sum of object/image area ratio. Position (x,y axis of center point) histogram.
Code to produce figure:
[objectnames, instancecounts, areacounts, pointcounts, positions]=LHIobjectstats(D, HOMEIMAGES, NEWHOMELABELMAPS); |
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Figure 2 Histogram of the number of key points used to define each object |
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Figure 3. Histogram of the percentage of pixels occupied (relative to the image size) by each object instance |
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| Figure 4. This plot shows the distribution of locations occupied by each instance. Each dot corresponds to the original location, relative to the image frame, of each object instance. |
Related Publications :
J. Porway, Q. Wang and SC. Zhu A Hierarchical Aerial Image Parsing. Proc. IEEE. Conf. on Computer Vision and Pattern Recognition (CVPR), June, 2008 |
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