Image Retrieval Using Ann

Image Retrieval Based on Color and Texture Feature Using Artificial Neural Network
Syed Sajjad Hussain#1, Manzoor Hashmani#2, Muhammad Moin uddin#3
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Faculty of Engineering, Sciences and Technology, IQRA University, Karachi
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[e-mail], [e-mail], [e-mail] Abstract. Content-based image retrieval CBIR is a technique that helps in searching a user desired information from a huge set of image files and interpret user intentions for the desired information. The retrieval of information is based on features of image like colour, shape, texture, annotation etc. Many of the existing methods focus on the feature extraction and to bridge up the gap between low level features and high level semantics. In this paper we propose a supervised machine learning (SML) using artificial neural network (ANN) and singular value decomposition (SVD) for image retrieval. Specifically we use back propagation algorithm (multilayer perceptron) (MLP) for training and testing our proposed model. Experimental results show that by changing parameters of feature vector back propagation method can have 62% precision instead of 49% as claimed by in Hyoung Ku LEE, Suk In Yoo [1]. Keywords Colour based image retrieval, back propagation algorithm, artificial neural network based image retrieval, multilayer perceptron (MLP).

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Introduction

Previously the information was primarily text based. But with the rapid growth in the field of computer network and low cost permanent storage media, the shapes of information become more interactive. The people are accessing more multimedia files than the past. In past, images, videos and audio files were only used for the entertainment purpose but nowadays these are the major source of information. Because of intense dependency on multimedia files for information searching, to obtain a desired result is a major problem as the search engine searches within the text associated with the multimedia files, instead

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