International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
A Survey on Techniques used for Content Based Image Retrieval Halah Ozhakkal Latheef1, Ambili. K2 1M.Tech
Scholar, Dept.of Computer Science & Engineering, CCET, Kerala,India Dept.of Computer Science & Engineering, CCET, Kerala, India
2Professor,
---------------------------------------------------------------------***--------------------------------------------------------------------2. TECHNIQUES OF CBIR Abstract - Development of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images from large image databases has also become more challenging. Since the past few years, Content Based Image Retrieval (CBIR) gained increased attention from researcher. CBIR is a system which uses visual features of images in large image databases and performs user’s requests. Important features of images are color, texture and shape which give detailed information about the image. CBIR techniques using different feature extraction techniques are being reviewed in this paper.
Indexing and Retrieval are the two important features of CBIR. Color, shape and texture are the most important features of an image. From these, feature vectors are extracted and these vectors are used for indexing purposes.
2.1 COLOR AND TEXTURE FEATURES FOR CONTENT BASED IMAGE RETRIEVAL A retrieval mechanism using color and texture [2] is being proposed here. Depending on the characteristic of the image texture, it can be represented by multiwavelet transform. The color correlogram in the RGB color space is chosen as the color feature. The main motivation of this system is to use the MultiWavelet decomposition scheme and color correlogram, which yield improved retrieval performance. Through the combination of Multi wavelet decomposition and color correlogram[2] we can increase the number of features, which in turn improves the retrieval accuracy. To support the efficient and fast retrieval of similar images from image databases, feature extraction plays an important role. The technique used for comparing images plays the fundamental ingredient of content based image retrieval. To create the feature vector, computed standard deviation of each sub-band is used. Then to find similarity between images, Euclidean distance metric is used. The average retrieval efficiency using this method is 75%. The main advantage is that it yields a large number of sub bands and hence improves the retrieval accuracy. A limitation is in its feature set.
Key Words: Color, Texture, shape features, Text based retrieval, CBIR.
1. INTRODUCTION CBIR, Content Based Image Retrieval has been an important area of research in the last few decades. For services to be efficient in all fields such as government, academics, hospitals, crime prevention, engineering, architecture, journalism, fashion and graphic design they make use of images. Due to its popularity, this digital image database becomes huge databases, and to search and retrieve specific images from these huge databases becomes difficult and time consuming. Traditionally, to solve these problems text-based retrieval were used. To search images, the user provides keywords as query terms and the system will return images similar to the query . In text based image retrieval keywords, label, tag or any information associated with the image is used for this metadata image retrieval . In this method query is entered in text format. But, there are limitations to this type of image retrieval system. Annotation of each database image requires domain experts who add label or other information to the image. Use of different keywords for annotation of each image in large databases is a highly time consuming process. It is also necessary to use unique keyword for annotation of each image which is a very complex task. Text descriptions are sometimes incomplete because they cannot very well depict complicated image features . A language mismatch can occur when the user and the domain expert uses different languages.
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2.2 CONTENT BASED IMAGE RETRIEVAL USING COLOR AND SHAPE FEATURES In this paper, an algorithm is proposed which incorporates the advantages of various other algorithms to Improve the retrieval accuracy and performance. The accuracy of color histogram based matching can be improved by using Color Coherence Vector (CCV)[3] for successive refinement. The speed of shape based retrieval can be enhanced by considering approximate shape rather than the exact shape. In addition to this a combination of color and shape based retrieval is also included to improve the accuracy.
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