Technique | StructuredCollection DS |
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Document | MPEG-7 Final Committee Draft MDS, see StructuredCollection DS |
Name | Ana Belen Benitez, Columbia University |
ana@ee.columbia.edu | |
Type | Application |
External Libraries | None |
Related Ds/DSs | Collection DSs, Model DSs, and ClusterModel DS |
Used Ds/DSs | ColorHistogram D |
Input | The input is a text file that describes the classification of M images into N classes with the following format: [Number of classes] [Class name 1 without spaces] [Number of images in the class] [image 1] [image 2] ... [Class name 2 without spaces] [Number of images in the class] [image 1] [image 2] ... ... [Class name N without spaces] [Number of images in the class] [image 1] [image 2] ... An example of a well formed input file with four classes follows: 4 Grass 3 cultu~44_add2.jpg cultu~45_add2.jpg cultu~46_add2.jpg Basketball 2 game111_add2.jpg game1191_add2.jpg Person 3 i0132_add5.jpg i0122_add5.jpg i8h_add1.jpg Dog 1 i8m_add1.jpg |
Extraction | Yes |
Client Appl | Search & Retrieval |
Summary | This code generates collection descriptions with associated probability models for each class (sub-collection) in the input and the entire collection of M images, and relations among the collections. The probability models associated with a collection consist of the mean and variance color histogram of the images in the collection. This code also allows to retrieve collection structure descriptions but matching based the closes two collections based on their mean color histogram. |
Strong Points | - |
Limitations | None |
Parameters | None |