CONTENT BASED IMAGE RETRIEVAL (CBIR) SYSTEM

Advancement in hardware and telecommunication technology has boosted up creation and distribution of digital visual content. However this rapid growth of visual content creations has not been matched by the simultaneous emergence of technologies to support efficient image analysis and retrieval. Although there are attempt to solve this problem by using meta-data text annotation but this approach are not practical when it come to the large number of data collection. This system used 7 different feature vectors that are focusing on 3 main low level feature groups (color, shape and texture). This system will use the image that the user feed and search the similar images in the database that had similar feature by considering the threshold value. One of the most important aspects in CBIR is to determine the correct threshold value. Setting the correct threshold value is important in CBIR because setting it too low will result in less image being retrieve that might exclude relevant data. Setting to high threshold value might result in irrelevant data to be retrieved and increase the search time for image retrieval. Result show that this project able to increase the image accuracy to average 70% by combining 7 different feature vector at correct threshold value. ii

A Novel Active Contour Model for Texture Segmentation

Texture is intuitively defined as a repeated arrangement of a basic pattern or object in an image. There is no mathematical definition of a texture though. The human visual system is able to identify and segment different textures in a given image. Automating this task for a computer is far from trivial. There are three major components of any texture segmentation algorithm: (a) The features used to represent a texture, (b) the metric induced on this representation space and (c) the clustering algorithm that runs over these features in order to segment a given image into different textures. In this paper, we propose an active contour based novel unsupervised algorithm for texture segmentation. We use intensity covariance matrices of regions as the defining feature of textures and find regions that have the most inter-region dissimilar covariance matrices using active contours. Since covariance matrices are symmetric positive definite, we use geodesic distance defined on the manifold of symmetric positive definite matrices PD(n) as a measure of dissimlarity between such matrices. We demonstrate performance of our algorithm on both artificial and real texture images

A New Colour-Texture Feature Extraction Method for Image Retrieval System Using Gray Level Co-occurrence Matrix

Proposed a new colour-texture feature extraction method is presented for Content Based Image Retrieval (CBIR) system using Gray Level Co-occurrence Matrix (GLCM). In this method, Colour-GLCM  (C-GLCM) is extracted from each colour channel, and then computes the average of each column of GLCM matrix for each channel. In this case, we will get a feature vector include colour and texture features at the same time to achieve the objectives of any CBIR system which are; decrease the Feature Vector (FV) dimensions which consequently reduces retrieval time, and also increase the retrieval accuracy.  To perform the evaluation of the proposed CBIR system, 4000 test images have been used as query images including 500 original images were selected randomly from image database of Iraqi National Museum of Modern Art, then applying seven image transformations on each original image resulting 3500 transformations image sued as query image. The proposed C-GLCM algorithm has led to improve and increase the retrieval accuracy (93.63%) comparing with GLCM that extraction from whole gray image (87.88%) and comparing with statistical properties that extraction from GLCM feature (80%)

Texture and Color Feature Extraction Form Ceramic Tiles for Various Flaws Detection Classification

Image analysis involves investigation of the image data for a specific application. Normally, the raw data of a set of images is analyzed to gain insight into what is happening with the images and how they can be used to extract desired information. In image processing and pattern recognition, feature extraction is an important step, which is a special form of dimensionality reduction. When the input data is too large to be processed and suspected to be redundant then the data is transformed into a reduced set of feature representations. The process of transforming the input data into a set of features is called feature extraction. Features often contain information relative to color, shape, texture or context. In the proposed method various texture features extraction techniques like GLCM, HARALICK and TAMURA and color feature extraction techniques COLOR HISTOGRAM, COLOR MOMENTS AND COLOR AUTO-CORRELOGRAMare implemented for tiles images used for various defects classifications

Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features

Texture classification became one of the problems which has been paid much attention on by image processing scientists since late 80s. Consequently, since now many different methods have been proposed to solve this problem. In most of these methods the researchers attempted to describe and discriminate textures based on linear and non-linear patterns. The linear and non-linear patterns on any window are based on formation of Grain Components in a particular order. Grain component is a primitive unit of morphology that most meaningful information often appears in the form of occurrence of that. The approach which is proposed in this paper could analyze the texture based on its grain components and then by making grain components histogram and extracting statistical features from that would classify the textures. Finally, to increase the accuracy of classification, proposed approach is expanded to color images to utilize the ability of approach in analyzing each RGB channels, individually. Although, this approach is a general one and it could be used in different applications, the method has been tested on the stone texture and the results can prove the quality of approach.Comment: The International Journal of Multimedia & Its Applications (IJMA) Vol.3, No.3, August 201

Texture fusion for batik motif retrieval system

This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object

Binary Wavelet Transform Based Histogram Feature for Content Based Image Retrieval

In this paper a new visual feature, binary wavelet transform based histogram (BWTH) is proposed for content based image retrieval. BWTH is facilitated with the color as well as texture properties. BWTH exhibits the advantages of binary wavelet transform and histogram. The performance of CBIR system with proposed feature is observed on Corel 1000 (DB1) and Corel 2450 (DB2) natural image database in color as well as gray space. The results analysis of DB1 database illustrates the better average precision and average recall of proposed method in RGB space (73.82%, 44.29%) compared to color histogram (70.85%, 42.16%), auto correlogram (66.15%, 39.52%) and discrete wavelet transform (60.83%, 38.25%). In case of gray space also performance of proposed method (66.69%, 40.77%) is better compared to auto correlogram (57.20%, 35.31%), discrete wavelet transform (52.70%, 32.98%) and wavelet correlogram (64.3%, 38.0%). It is verified that in case of DB2 database also average precision, average recall and average retrieval rate of proposed method are significantly better