| 15 Iraqi Journal for Computers and Informatics Vol. [45], Issue [1], Year (2019) ) COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW Sarmad T. Abdul-samad 1 1 AL-Nahiriain University / / Computers Science Department Baghdad, Iraq sarmad.thaer991@rdd.deu.iq Sawsan Kamal 2 2 AL-Nahiriain University / / Computers Science Department Baghdad, Iraq skt@sci.nahra1inuniiv.edu.iq Abstracts: Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for the researchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shape features. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color feature is the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely Local Color Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors features taking in consideration the spatial information of the image. Keywords: Spatial Features, Color Histogram, Color Correlogram, Color Coherence Vector I. INTRODUCTION In the last years, the development of the multimedia applications led to widespread off digital images. Also, the developing of then images’ sharing unlimited number of images via social media every day. However, managing and organizing these digital images present a problem. Thus, the concepts of indexing and retrieval were introduced to overcome this issue. Indexing relates to “how images are store in database to retrieve them (through querying) more efficient”, whereas Retrieval relates to “how to retrieve images that are most relevant to the query image from images in database” [1,2,3]. At the First, Texts-Based Images Retrieval (TBISR) are used to achieve the image retrieval task. It’s depend one metadata that related to each image and the retrieving of image task done by using traditional query techniques “using keyword”. This method works well with small digital images databases but, it has low efficiency with huge database. The most important problem in TBIR is different users use different words to describe the same image (subjectivity of the human). This problem negatively affects the efficiency of the text-based image search, so, a need for more efficient image retrieval system is appeared. The needed system must perform an automatic indexing and retrieving. Therefore, the second method depending on image content for indexing and retrieving. Therefore, this method is generally known as Contents-Based Images Retrieval (CBIIR) [4]”.” II. CONTENTS-BASED IMAGES RETRIEVALS CBJIR was introduced in the 1990’s. It depending one analysis of the image’s visual content which can be analyzed by extracting image features such as color, texture and shape that are called low level features [5]. In order to design and implement generic CBIR applications, both advanced algorithms in image understanding field and advances in computer hardware is needed, which are unrealized at this time [6,7]. Therefore, most efforts are directed to a specific CBIR applications [6,7]. A wide range of CBIR applications varied from personal to medical diagnoses, crime prevention, education, military and many other applications [8]. Figure 1 shows CBIR system steps. Figure (1): CBIR System Diagram [9]