An Integrated Relevance Feedback Method for CBIR Using Histogram Values, Texture Descriptor and Interactive Boosting

Title: An Integrated Relevance Feedback Method for CBIR Using Histogram Values, Texture Descriptor and Interactive Boosting
Publisher: Guru Nanak Publications
ISSN: 2278-0947
Series: Volume 2 Issue 2
Authors: V. V. S. S. S. Balaram, Kranthi Kumar.K and Sunil Bhutada


Abstract

Image retrieval is an important topic in the field of pattern recognition and artificial intelligence. There are three categories of image retrieval methods: text-based, content-based and semantic-based. In CBIR, images are indexed by their visual content, such as color, texture and shapes. Color and Texture information have been the primitive image descriptors in content based image retrieval systems. Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In this paper, an integrating Relevance feedback for content based image retrieval based method is proposed for image mining based on analysis of color Histogram values and texture descriptor of an image and a novel interactive boosting framework to integrate user feedback into boosting scheme and bridge the gap between high-level semantic concept and low-level image features. For this purpose, three functions are used for texture descriptor analysis such as entropy, local range and standard deviation. To extract the color properties of an image, histogram values are used. The combination of the color and texture features of the image provides a robust feature set for image retrieval. Our method has advantage over the classic relevance feedback method in that the classifiers are trained to pay more attention to wrongfully predicted samples in user feedback through a reinforcement training process. It achieves more performance improvement from the relevance feedback than AdaBoost does because human judgment is accumulated iteratively to facilitate learning process.

Keywords

Boosting, Content-based image retrieval, Color histogram, Image text, Image classification, Information retrieval, Pattern recognition, Reinforcement training, Relevance feedback.

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