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A Low Indexed Content Based Neural Network Approach for Natural Objects Recognition


Affiliations
1 JNTUH, Hyderabad, AP, India
2 JNTUH, College of Engineering, jagitial, Karimnagar, AP, India
3 Chaithanya Institute of Engineering and Technology, Kakinada, AP, India
 

In this paper, an approach to integral color texture invariant information with a neural network approach to object recognition is proposed. A color-texture context for image retrieval system based on the integral information of an image is represented as one compact representation base on color histogram approach. A general and efficient design approach using a neural classifier to cope with small training sets of high dimension, which is a problem frequency encountered in object recognition, is focused in this paper for general images. The proposed system is tested for various colored image samples and the recognition accuracy is evaluated.

Keywords

Color-Texture Context, Recognition System, Histogram, Neural Network.
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  • A Low Indexed Content Based Neural Network Approach for Natural Objects Recognition

Abstract Views: 279  |  PDF Views: 161

Authors

G. Shyama Chandra Prasad
JNTUH, Hyderabad, AP, India
A. Govardhan
JNTUH, College of Engineering, jagitial, Karimnagar, AP, India
T. V. Rao
Chaithanya Institute of Engineering and Technology, Kakinada, AP, India

Abstract


In this paper, an approach to integral color texture invariant information with a neural network approach to object recognition is proposed. A color-texture context for image retrieval system based on the integral information of an image is represented as one compact representation base on color histogram approach. A general and efficient design approach using a neural classifier to cope with small training sets of high dimension, which is a problem frequency encountered in object recognition, is focused in this paper for general images. The proposed system is tested for various colored image samples and the recognition accuracy is evaluated.

Keywords


Color-Texture Context, Recognition System, Histogram, Neural Network.