COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW

s: 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.


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.

Indexing -"
The task of organizing the images' database using a specific indexing structure for retrieval.
Retrieval -The retrieval takes can be performed more effective on the structured database.

III. COLOR FEATURE
Color feature considered as one of the most widely used visual features in CBIR, and can visually be recognized by the humans. Color results from the light interaction with a human eyes and brain. Human mostly based on color feature to distinguish the images. It is also used as a powerful descriptor that simplifies object identification [10]. Color feature are easy to extract and it is found to be effective for indexing and searching colored images. To extract color feature from the image, a proper color space (also called color module) such as RGB (Red, Green, and Blue), HSV (Hue, Saturation, and Value) must be selected and also an effective descriptor should be determined [11].

Locals Colors Histogram (LCH)
Then most widely used technique for color feature extracting off and image is colors histograms. It represents the image from a different perspective. Its represents them frequency distribution off color's bins inn and image. Its count's simila1r pixe1ls and store's it. There are tw1o types off color's histograms. Globa1l colo2r histograms and loca1l col1or histograms. Co1lor histogr1am is proposed ass and global1 color descriptor which analyze every statistica1l colors frequency inn and images". Local color histogram focuses them colo1rs distributions of regions. The spatial distribution off pixel is taken in calculation when using LCH, which is not calculated when using Global colors histogram. It calculated by segments the image into many segments or fix parts and then, the histogram is calculated these segments. Image will then be represented by whole histograms [4].

Color Correlorgram
Spatial information of the extracted feature is the main drawback of the color histogram. For example, all the images shown in figure 2 have the same color proportion but, different spatial distribution Correlation histogram (correlogram) tries to fix this histogram's drawback by taking the spatial correlation of color distribution into account, and shows how them spatial correlation between pairs off colors are changing with distances [13].
A color correlogram can be represented as a

Colors" Coherences Vectors
Inn them colors coherences vectors (CCV) the images' pixels are partitioned according to their spatial coherence into two groups, i.e., coherent or in-coherent". If those pixels belong to a large uniformed color region, it's called coherences, otherwise it in-coherence. After calculating the CCV separate histograms can be produced for both coherent and incoherent pixels. CCV having some problem, but the main problem and the most time consuming is the computation of three dimensional index vector. To calculate the index vector, all image's pixels must be compared with all of its adjacent pixels to find out whether this pixel is belong to coherence or incoherence group. Using CCV
a. Row Sum and Column Sum Row sum and column sum is another type of color spatial features. For any two similar images, the row sum and column sum are approximately equal. The row sum for any images can be calculated as follow: b. Color structure descriptors (CSD) Then CSHD represents colors contents information (colors histogram inn addition tow information about them structure off its content i.e. localized colors distributions using structuring windows10). These performances off CSZD relies one them size and structure off them window, which are difficult to specify Then CZSD is connected tow them double-coned HMTMD colors spaces which is quantized non-uniformly into 324, 646, 1283 or 2562 bins, this. feature guarantees cementing them colors structures information into them CSID, this is achieved buy considering all colors inn an structuring windows10 which slides over them imaged, instead off considering individual pixels separately. For example, bins ki off them histograms shows how\ many times them structuring elements contains at least one pixel with colors ki. If then windows1 is off size 11 pixels [18] [19]. has much more gray component in CSD description [20].

COMPAWRATIVE ANAHLYSIS
A comparative analysis off them color1 1feature extractions techniques1 with their advantages and disadvantages are shown inn tables1.  [26] An important issue while selecting them colors features extractions methods are storage space required\, scalability1, rotations invariants, computational2 times required and its feasibility, and efficient inn storages spaces and times. complexity parameters". Histograms is easy tow computes, but isn't robust and unique whereas using local color histogram solve a part of the problem since it gives a spatial information about each region of the image, and this approach is also computationally and easy approach. The correlogram solving the problem of histogram but it will increase the size of features' vector and effect the storage, and this will take more time. So, using the auto-correlogram will give approximately similar results and similar effects on the performance of the system with less values. In row sum and column sum the images must have same size in order to produce a same number of rows and columns for each image, these prove that these features are effected by the image size. Color coherence vector solve all the problems the previously discussed but it's computationally cost, to calculate the index vector, all image's pixels must be compared with all of its adjacent pixels to find out whether this pixel is belonging to coherence or incoherence group which is time consuming but success with CBIR systems due to its additional spatial information as mentioned in previous sections.

Conclusion
CBIR is one of them most important research topic inn patterns recognitions, images processing's and computers vision fields off study. Then CBIJR goals is to retrieve relevant images from images collected in database that can be done by measures them similarities between them query image's and them database's image's. "It is generally base's one analyzing then visuals content off them images depending one three basic's lows levels features, which are: colors, textures and shaper, and them color's is then most important1 one among them". Some off methods used tow extracts colors features are discussed inn this's papers. The selection of a method depends on its functionality for a specific purpose.