抽象的

AN IMPROVED LOCAL TETRA PATTERN FOR CONTENT BASED IMAGE RETRIEVAL

Thangadurai K, Bhuvana S, Dr Radhakrishnan R

Content-based image retrieval (CBIR)- an application of computer vision technique, addresses the problem in searching for digital images in large databases. This emerging approach includes the Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Local Ternary Pattern (LTP) and Magnitude Pattern. In this paper, local Tetra pattern (LTrP) for CBIR method based on horizontal and vertical direction and also includes the magnitude pattern refers the uniform pattern and non-uniform pattern (i.e all the pixel in an image) is proposed. Unlike the conventional method which encodes the relationship between the referenced pixel and its surrounding neighbours by computing gray-level difference and the magnitude pattern refers the uniform pattern only the proposed includes 1). Pre-processing and direction of pixel which uses the pre-processing technique namely resize and calculated the first order derivatives along with and. 2). Extraction of pattern using LTrP and LBP used to classify each pixel using tetra direction and separate into binary patterns 3). Extraction of magnitude pattern is collected using magnitudes of derivatives. 4). Finally, Hybrid method is established to extract the feature of image by combining LTrP, LBP and magnitude pattern which is use to improve the performance. The performance analysis shows that the proposed method improves the retrieval result from 73.4%/42.7% to 79.5%/47.8% in terms of average precision/average recall on database DB.