Journal article Open Access

Framework for Color and Texture Feature Fusion in Content Based Image Retrieval using Block Truncation Coding with Color Spaces

Sudeep D. Thepade; Rohan Awhad; Prakhar Khandelwal

Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)

With tremendous growth in social media and digital technologies, generation, storing and transfer of huge amount of information over the internet is on the rise. Images or visual mode of communication have been prevailing and widely accepted as a mode of communication since ages. And with the growth of internet, the rate at which images are generated is growing exponentially. But the methods used to retrieve images are still very slow and inefficient, compared to the rate of increase in image databases. To cope up with this explosive increase in images, this information age has seen huge research advancement in Content Based Image Retrieval (CBIR). CBIR systems provide a way of utilizing the 3 major ways in which content is portrayed in images, those are shape, texture and color. In CBIR system, features are extracted from query image and similarity is found with features stored in database for retrieval. This provides an objective way of image retrieval, which is more efficient compared to subjective human annotation. Application specific CBIR systems have been developed and perform really well, but Generic CBIR systems are still under developed. Block Truncation Coding (BTC) has been chosen as a feature extractor. BTC applied directly on input image provides color content-based features of image and BTC applied after applying LBP on the image provide texture content-based features of image. Previous work consists of either color, shape or texture, but usage of more than one descriptor is still in research and might give better performance. The paper presents framework for color and texture feature fusion in content-based image retrieval using block truncation coding with color spaces. Experimentation is carried out on Wang Dataset of 1000 images consisting of 10 classes. Each class has 100 images in it. Obtained results have shown performance improvement using fusion of BTC extracted color features and texture features extracted with BTC applied on Local Binary Patterns (LBP). Conversion of color space from RGB to LUV is done using Kekre's LUV.

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