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Home > FreeData Preview > LHI_ObjectsinScene
 
Dataset 6 LHI_ObjectsinScene
Contents and Statistics :
Category
Number of Images
Free
Total
Highway
10
205
Kitchen
10
119
Meeting
10
148
Office
10
169
Parking
10
266
Street
10
403
Matlab codes to load this category (see MatlabToolbox page for more information):
clear; close all;
HOMEIMAGES = 'C:\Yourdatabase\LHI_ObjectsinScene\Images';
% set your folder
HOMEANNOTATIONS = 'C:\Yourdatabase\LHI_ObjectsinScene\Annotations';
HOMELABELMAPS = 'C:\Yourdatabase\LHI_ObjectsinScene\LabelMaps';

Code to load the database and mapping the names
rawDB = LHIdatabase(HOMEANNOTATIONS); %To load the database
NEWHOMELABELMAPS = 'C:/temp/yourposition/'; %folder to store regulated labelmap
D=LHIregulatenames(rawDB,HOMELABELMAPS,NEWHOMELABELMAPS,'Sport_names.txt'); %name translation, you can define your own name translation dictionary by modifying Sport_names.txt


Figure1 Name table of all categories and correspond color of in labelmaps. Left three columns are R,G,B values respectively.


Code to produce the figure below:
LHIobjectnames(D); %show object frequency

Figure 2 Object frequency count

View one image for hierarchical decomposition
Code to produce the figure:
LHIdbshowimage(D,3,HOMEIMAGES); %Display hierarchical decompostion of one image
Figure 3. Hierarchical decomposition of an image

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);
Figure 4 Histogram of the number of key points used to define each object
Figure 5. Histogram of the percentage of pixels occupied (relative to the image size) by each object instance
Figure 6. 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. These plots help to understand some of the biases that the photographers might have when taking pictures of specific objects.


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