Image Retrieval using Modified Block Truncation Coding Feature
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Abstract
With the advent of digital camera, improvement in the digital storage media and rapid development in internet provide a huge collection of images. Fast and accurate image retrieval from this huge collection of image database is a challenging task. This is possible only by combining the image retrieval mechanism along with image compression technique. Content Based Image Retrieval (CBIR) uses the visual information in the images to retrieve the similar images. In this paper, image feature descriptor from the compressed stream of image data is extracted where the images are compressed using a variant of Block Truncation Coding (BTC) named Dot Diffused Block Truncation Coding (DDBTC). Here the Colour Histogram Feature (CHF) and the Bit Pattern Features (BPF) are extracted and used to represent the image and similarity computation.
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