Abstract
Automatic image categorization and description are key components for many applications, i.e., multimedia database management, web content analysis, human-computer interactions, and biometrics. In general, image description is a difficult task because of the wide variety of objects potentially to be recognized and the complexity and variety of backgrounds. This paper introduces a computational model for context-based image categorization and description. First, for a given image, a classifier is trained by the associated text features using advanced concepts, so that it can assign the image to a specific category. Then, a similarity matching with that category's annotated templates is performed for images in every other category. The proposed model uses novel text and image features that allow it to differentiate between geometrical images (GIs) and ordinary images. The experimental results show that the model is able to categorize correctly images with an expected increase in similarity matching as larger datasets and neural document classifier (NDC) are used. An important feature of the proposed model is that its specific matching techniques, suitable for a particular category, can be easily integrated and developed for other categories.
Original language | English |
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Article number | 1250001 |
Journal | International Journal of Image and Graphics |
Volume | 12 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2012 |
Bibliographical note
Publisher Copyright:© 2012 World Scientific Publishing Company.
Keywords
- Image categorization
- context-based image description
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Computer Graphics and Computer-Aided Design