2012 IEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) An Effective Content Based Image Retrieval (CBIR) System Based on Evolutionary Programming (EP) *Sunita Manoj Jadhav,**Dr.Vikram Patil *Assistant Professor Department ofEXTC Saaswati college of Engineering Kharghar, Navi Mumbai. **Principal SanjeevanEngineering and Technology Institute Panhala, Kolhapur. *sunitaanoladhavphd@gmail.com, * *vsp.research@gmail.com Abstract- This paper introduces a content Based Image Retrieval (CBm) based on evolutionary algorithm. Initially, the shape, color and texture feature is extracted for the given query image and also for the of the database images in a similar manner. Subsequently, similar images are retrieved utilizing an evolutionary algorithm based similarity. Thus, by means of the evolutionary algorithm, the required relevant images are retrieved from a large database based on the given query. The proposed CBIR system is evaluated by querying different images and the efciency of the proposed system is evaluated by means of the precision-recall value of the retrieved results. Kewords- feature etraction, shape, color histogram teure, query, Evolutionar Pogramming (EP) I. INTRODUCTION Content Based Image Retrieval (CBIR) is an important research area for manipulating large multimedia databases and digital libraries [1] [2] characterized by automatic indexing of images based on their own visual features [7] [14] [15]. Commonly used features include [3] color, texture, shape, and edge information. Many retrieval methods that accept query images as input fom the user represent images as vectors in the feature space and search for images based on their features and feature representations [13]. When the user presents a sample query image, region of interest (ROI), or pattern to the system, it performs various visual query mechanisms, such as the query-by example (QBE) paradig, and fnally outputs the relevant images [6]. In CBIR, image content is fequently represented using image features [5]. CBI fnds applications in internet, advertising, medicine, crime detection, entertainment, and digital libraries. High retieval efciency and less computational complexity are the desired characteristics of CBI system and they are the key objectives in the desig of a CBIR system [4]. Many CBI systems have been proposed in the past decade, including QBIC, Virage, Photohook, Visual SEEk, Netra, MARS, and so on. Unfortunately, there are still many problems that hinder CBI systems fom being popular [9] [10]. A CBI system should efectively capture the user's preference on retrieval by well characterizing a mapping fom image features to human concepts [S]. Two major research communities that are concerned with image retrieval are database management and computer vision. While one of these two research communities deals with text-based image retrieval, the other is associated with ISBN No. 97S-1-4673-204S-1112/$31.00©2012 IEEE visual-based image retrieval [11]. However, there exist two major diffculties, especially when the size of the image collection is large (tens or hundreds of thousands). One is the large amount of labor required in manual image annotation, whereas the other one, which is more important, results fom the rich content in the images and the subjectivity of human perception [12]. Traditionally, the majority of CBI systems utilize any one or two feature of the image for analyzing the similarity and due to this the efectiveness of the CBI system is degraded. Hence to overcome this, in this paper we extract an extensive feature set and for retrieving the relevant images we utilize the evolutionary programming method. Te color, shape and texture features are exracted in the feature extraction process. Consequently, evolutionary programming is applied to obtain the relevant images for the given query image. Te rest of the paper is organized as follows section 2 reviews the recent research works related to CBIR techniques. Te steps involved in our proposed work with mathematical formulation and pictorial representation are detailed in section 3. Section 4 discusses about the simulation results. Section 5 concludes the paper. II. RECENT RELATED RE SEARCHES: A REVIEW A handfl of recent researches that are related to the proposed concept are discussed in detail in this section. Hiremath et al. [16] have presented a method for salient points determination based on color saliency. The color and texture information around these points of interest have served as the local descriptors of the image. In addition, the shape information has been captured in terms of edge images computed using Gradient Vector Flow felds. Invariant moments have been used to record the shape features. The combination of the local color, texure and the global shape features has provided feature set for image retrieval. Arti Khaparde et af [17] have presented an approach for global feature extraction using Independent Component Analysis (ICA). A comparative study has been done between ICA feature vectors and Gabor feature vectors for ISO different texture and natural images in a databank. Result analysis has shown that extracting color and texture information by ICA has provided sigifcantly improved results in terms of retrieval accuracy, computational complexity and storage space of feature 310