International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 5 (2018) pp. 2432-2442 © Research India Publications. http://www.ripublication.com 2432 A Fuzzy Logic Based Soft Computing Approach in CBIR System Using Incremental Filtering Feature Selection to Identify Patterns 1 V. Yadaiah and 2 Dr. R .Vivekanandam, .Rajaram Jatothu 3 1,3 Research Scholar, Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Sciences, Sehore, M.P., India. 2 Professor, Director in Muthayammal Eng. College, Rasipuram, Namakkal,T.N., India Abstract Content based Image Retrieval (CBIR) may be a set of techniques for retrieving semantically-relevant pictures from an image database supported automatically-derived image options. Generally, in CBIR systems, the visual features are described at low-level. They are simply rigid mathematical measures that cannot influence the inherent subjectivity and fogginess of individual’s understandings and perceptions. As a result, there is a niche between low-level features and high- level semantics. We have a tendency to are witnessing the era of massive information computing where computing the resources is turning into the most bottleneck to handle those massive datasets. With in the case of high dimensional data where every view of information is of high spatiality, feature selection is important for additional rising the clustering and classification results. In this paper, we have a tendency to propose a new feature selection method is Incremental Filtering Feature Selection (IFFS) algorithm that employs the Fuzzy Rough Set for choosing best subset of features and for effective grouping of huge volumes of data, respectively. We introduce a new system of visual features extraction and matching by using Fuzzy Logic (FL). FL is a powerful tool that deals with reasoning algorithms used to emulate human thinking and decision making in machines. An in depth experimental comparison of the proposed method and other methods are done. The performance of the proposed model yields promising results on the feature selection, and retrieval accuracy in the field of Content based Image Retrieval. Keywords: Content Based Image Retrieval, Fuzzy Logic, Fuzzy Color, and Incremental Filtering Feature Selection. INTRODUCTION Very massive collections of images are growing quickly because of arrival of cheaper storage devices and also the internet. Finding an image from a huge set of images is very challenging task. One solution to this problem is to label images manually. But it is too expensive, time consuming and not feasible for several applications. Moreover, the labeling process depends on the semantic accuracy in describing the image. Therefore, many content based image retrieval systems are developed to extract low levels features for describing the image content [1]. A typical content-based retrieval system (as in Fig.1) is split into 2 stages: off-line feature extraction and on-line image retrieval [2]. In off-line stage, the system mechanically extracts visual attributes of every image in the database based on its pixel values and stores them in a different database inside the system, known as a feature database. In on-line stage, the user will submit a query example to the retrieval system. The system represents this example with a feature vector. The distances (i.e., similarities) between the feature vectors of the query example and those of the media in the feature database are then computed and ranked. The system ranks the search results and then returns the results which are most similar to the query examples. Image data is vague in nature and in content-based retrieval this property creates some issues like [3]: 1. Descriptions of image contents typically involve inexact and subjective concepts. 2. Typically imprecision and vagueness exist in descriptions of the images and in some of the visual features. 3. User’s needs to image retrieval could also be naturally fuzzy. Fuzzy Logic (FL) is used in CBIR system because it is the character of of image data, and also the nature of human perception and thinking process. So, it can minimize semantic gap between high level semantic and low level image features. Also, it is robust to the noise and intensity modification in the images. Finally, the users are interested in results according to similarity (closeness) instead of equality (exactness). In [4], a color histogram representation, called Fuzzy Color Histogram (FCH), is presented by considering the color similarity of each pixel’s color associated to all the histogram bins through fuzzy-set membership function. An approach for computing the membership values based on fuzzy-means algorithm is developed. The proposed FCH is further exploited in the application of image indexing and retrieval. Konstantinidis et al [5] proposed a fuzzy linking system for color histogram creation in L*a*b* color space. It contains 10 bins, and 27 rules used to derive the final histogram. Kucuktunc et al [6] proposed a fuzzy linking system for color histogram creation in L*a*b* color space. Their system contains 15 bins, and 27 rules used to derive the final histogram.