Author(s): DHANYA BIBIN and PUNITHA P
Journal: International Journal of Machine Intelligence
ISSN 0975-2927
Volume: 3;
Issue: 4;
Start page: 225;
Date: 2011;
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Keywords: CBIR | Image classification | Artificial neural networks(ANN) | COREL database | Color histogram | Color moments | Color coherence vector | Edge direction histogram
ABSTRACT
In this paper high-level image classes are inferred from low-level image features like color and shape features with the help of artificial neural network. Back propagation neural network algorithm is used for integrating knowledge from low-level image features and classify the images into high level concepts / semantic classes. The classifier is evaluated on a database of 1000 images from COREL database. The experimental results show that the accuracy using back propagation neural network algorithm to classify COREL images ranges between 80.5% to 88.6%.
Journal: International Journal of Machine Intelligence
ISSN 0975-2927
Volume: 3;
Issue: 4;
Start page: 225;
Date: 2011;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: CBIR | Image classification | Artificial neural networks(ANN) | COREL database | Color histogram | Color moments | Color coherence vector | Edge direction histogram
ABSTRACT
In this paper high-level image classes are inferred from low-level image features like color and shape features with the help of artificial neural network. Back propagation neural network algorithm is used for integrating knowledge from low-level image features and classify the images into high level concepts / semantic classes. The classifier is evaluated on a database of 1000 images from COREL database. The experimental results show that the accuracy using back propagation neural network algorithm to classify COREL images ranges between 80.5% to 88.6%.
