© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2486 An Approach of K-means Clustering Based SVM ensemble for Image Retrieval Shalini Soni 1 , Punit Kumar Johari 2 1,2 Department of CSE/IT, MITS Gwalior, MP, India -------------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Content Based Image Retrieval is important for retrieving the visual relevant image from the huge database. Color, Texture, Shape information has been primitive image descriptors in CBIR. In Content-Based Image Retrieval System, the visual feature (Color, Texture, and Shape) are represented at a low level. In this paper, we have extracted the feature of technique with using K-means clustering and use the SVM classifier. Key Words: Content Based Image Retrieval, Feature Extraction Technique, Color, Texture, and Shape, K-means clustering, SVM. 1. INTRODUCTION With the improvement of digital image and videos, CBIR has become an important research area to search and retrieval useful information. Content-Based Image Retrieval is the process of the retrieving the image of the huge database and using the extraction feature methods [1]. CBIR includes the digital image, video, audio, graphics and the text data. We have many ways to retrieve an image. Feature Extraction is one function of CBIR its mean mapping the image pixels into the feature spaces. Image retrieval is a technique which is concerned with searching & browsing digital image from the huge database. Image database uses in many fields like as medicine, biometric security, satellite image processing, military, and security purpose. 2. CONTENT BASED IMAGE RETRIEVAL (CBIR) The term Content-Based Image Retrieval (CBIR) was originated in 1992. It was used by T. Kato to describe experiments into automatic retrieval of an image from a database, based on Color & Shape present [2]. Content- Based Image Retrieval is an application of the computer vision and it is used to retrieve the image in the huge database. “Content-baseddz means searching of the contents of an image rather than the metadata like as keywords, tag, or characterization related with the image. Content-Based Image Retrieval represent in the form of figure 1. Content- Based Image Retrieval is defining the two type categorized. (a) Text Query: Image is defining the form of the Text such as keywords and caption. Text features effectively as a query if suitable text descriptions are given for descriptions in an image database. (b) Pictorial query: An example of an adopted image is used as a query. To restore related image like an image feature such as color, a texture is used [3]. Fig -1: Architecture of Content Based Image Retrieval 3. LITERATURE REVIEW In 2015, Suresh MB, Dr. Mohan Kumar Naik; Proposed color space feature texture method for image retrievals, such as RGB HSV and YCbCr. Texture feature is extracted by applying GLCM [4]. In 2017, Miss Dhanshree S. Kalel, Miss Pooja M. Pisal, Mr. Ramda P. Bagawade; Proposed the three visual feature Color , Texture, Shape to retrieve query image from large database color shape and texture extracted feature for given query image and measure the similarity from database image and retrieve the similar image as a query image extraction features [5]. In 2016, Ms. V. Ausha, Ms. V. Usha Reddy, Dr. T. Ramashri; Proposed an algorithm to retrieve the color image from the Query Image Feature Extraction Similarity Measure Retrieve- Image Image Database Feature Database International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 07 | July 2018 www.irjet.net p-ISSN: 2395-0072