International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 2425 A REVIEW ON CONTENT BASED IMAGE RETRIEVAL BASED ON SHAPE, COLOR AND TEXTURE FEATURES USING DWT, MODIFIED K-MEANS AND ANN Manisha Aeri 1 , Ashok Kumar 2 , H.L. Mandoria 3 , Rajesh Singh 4 1 M.Tech Student, Department of Information Technology, GBPUAT Pantnagar, Uttarakhand, India 2,4 Asst. Professor, Department of Information Technology, GBPUAT Pantnagar, Uttarakhand, India 3 Professor and Head, Department of Information Technology, GBPUAT Pantnagar, Uttarakhand, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Content based image retrieval (CBIR), as the name suggests is the retrieval of images based on some of the visual features like color, shape, texture etc. It has proved being a champion among the most remarkable research areas in the recent years and it's need can be found in various specific domains, for example, Data processing, Education, Medical Imaging, Crime bar, Weather surveying etc. This paper reviews the completely unique technique that uses an effective calculation for Content Based Image Retrieval (CBIR) in context of Discrete Wavelet Transform (DWT), Modified K- Means Clustering and Artificial Neural Network. There are two basic steps to be followed in CBIR i.e. feature extraction and similarity measurement. This paper comparatively utilizes wavelet transform which helps in the image compression and denoising. Image compression helps to reduce the storage space of images which can eventually increase the performance. Discrete wavelet transformation decreases the size of feature vector as well as preserve the content details.ANN is more effective and efficient algorithm for the similarity measurement and also ANN is used to train and test the proposed framework. The blend of DWT, Modified K-Means procedures and Neural Network expands the execution of image retrieval structure for shape, shading and surface based request. Trial happens demonstrate that the proposed plot has higher retrieval exactness than other traditional plans like Precision and Recall. Key Words: dwt, modified k means, ANN, Gabor filter, Haar wavelet 1. INTRODUCTION In early days as a result of extensive image accumulations the manual approach was more tough so as to beat these difficulties Content Based Image Retrieval (CBIR) was presented. Content-based image retrieval (CBIR) is the use of laptop vision to the image retrieval drawbacks. During this approach rather than being physically annotated by textual keywords in this approach pictures would be indexed utilizing their own particular visual contents .The visual contents might be shading, surface and shape. This approach is alleged to be a general framework of picture retrieval. There are three key bases for Content Based Image Retrieval which is visual component extraction, multidimensional categorization and retrieval system design. The shading viewpoint can be accomplished by the strategies like averaging and histograms. The surface angle can be accomplished by utilizing changes or vector quantization .The shape viewpoint can be accomplished by utilizing gradient operators or morphological operators. A picture retrieval system may be a system that permits us to browse, to make a search and retrieve the pictures. Content Based Image Retrieval is that the method of retrieving the required question image from a large range of databases that rely on the contents of the image. Color, texture, shapes and other native features are the square measures or the overall techniques used for retrieving a selected image from the pictures database. Content based mostly Image Retrieval systems works with all the pictures and therefore the search is based on comparison of features with the question image. The principle elements of CBIR are the features which incorporates the Geometric shapes, hues and the texture of the picture. There are basically two types of features that are global and local features. Object recognition should be possible effortlessly by utilizing the local features. The consequent element is the related content or text in which the pictures can likewise be recovered utilizing the content or text related with the picture. The other element is the relevant feedback wherever it helps to be a lot more precise in making the search of relevant pictures just by absorbing the feedbacks of the user. 1.1 Feature Extraction Feature extraction is a methodology that is applied to any image so that we can categorize and recognize the pictures from huge set of data on the basis of those features. The features can be color, shape, texture etc. 1.2 Color One of the most beneficial and distinguishing feature is the color. A color histogram methodology is more effective and efficient, that’s why it is more frequently used for CB)R. For more color histogram match HSV color space are used. The use of HSV color space is to manipulate the hue and saturation. 1.3 Color Histogram If the color pattern is unique and that color pattern is compared with the massive range of the data set in that case