Name
Abstract | ||
|---|---|---|
The success of a multimedia information system depends heavily on the way the data is represented. Although there are “natural” ways to represent numerical data, it is not clear what is a good way to represent multimedia data, such as images, video, or sound. In this paper, we investigate various image representations where the quality of the representation is judged based on how well a system for searching through an image database can perform—although the same techniques and representations can be used for other types of object detection tasks or multimedia data analysis problems. The system is based on a machine learning method used to develop object detection models from example images that can subsequently be used for examples to detect—search—images of a particular object in an image database. As a base classifier for the detection task, we use support vector machines (SVM), a kernel-based learning method. Within the framework of kernel classifiers, we investigate new image representations/kernels derived from probabilistic models of the class of images considered and present a new feature selection method which can be used to reduce the dimensionality of the image representation without significant losses in terms of the performance of the detection-search-system. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TKDE.2003.1209008 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
probability,probabilistic model,image retrieval,support vector machine,support vector machines,feature selection,learning artificial intelligence,machine learning | Data mining,Multimedia database,Feature selection,Feature detection (computer vision),Computer science,Image retrieval,Artificial intelligence,Classifier (linguistics),Kernel (linear algebra),Object detection,Pattern recognition,Support vector machine,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 4 | 1041-4347 |
Citations | PageRank | References |
30 | 1.48 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theodoros Evgeniou | 1 | 3005 | 219.65 |
| Massimiliano Pontil | 2 | 5820 | 472.96 |
| Constantine Papageorgiou | 3 | 1897 | 403.34 |
| Tomaso Poggio | 4 | 13488 | 3380.01 |