Image Retrieval Berdasarkan Fitur Warna, Bentuk, dan Tekstur

Along with the times, information retrieval is no longer just on textual data, but also the visual data. The technique was originally used is Text-Based Image Retrieval (TBIR), but the technique still has some shortcomings such as the relevance of the picture successfully retrieved, and the specific space required to store meta-data in the image. Seeing the shortage of Text-Based Image Retrieval techniques, then other techniques were developed, namely Image Retrieval based on content or commonly called Content Based Image Retrieval (CBIR). In this research, CBIR will be discussed based on color, shape and texture using a color histogram, Gabor and SIFT. This study aimed to compare the results of image retrieval with some of these techniques. The results obtained are by combining color, shape and texture features, the performance of the system can be improved

An Efficient CBIR Technique with YUV Color Space and Texture Features

In areas of government, academia and hospitals, large collections of digital images are being created. These image collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image retrieval system is presented in this paper, which used YUV color space and wavelet transform approach for feature extraction. Firstly, the color space is quantified in non-equal intervals, then constructed one dimension feature vector and represented the color feature. Similarly, the texture feature extraction is obtained by using wavelet. Finally, color feature and texture feature are combined based on wavelet transform. The image retrieval experiments specified that visual features were sensitive for different type images. The color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At the same time, experiments reveal that YUV texture feature based on wavelet transform has better effective performance and stability than the RGB and HSV. The same work is performed for the RGB and HSV color space and their results are compared with the proposed system. The result shows that CBIR with the YUV color space retrieves image with more accuracy and reduced retrieval time. Keywords---Content based image retrieval, Wavelet transforms, YUV, HSV, RG

Human-Centered Content-Based Image Retrieval

Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van den Broek et al., 2004, 2005). This benchmark was used to explore what and how several parameters (e.g., color and distance measures) of the CBIR process influence retrieval results. In contrast with other research, users judgements were assigned as metric. The online IR and CBIR system Multimedia for Art Retrieval (M4ART) (URL: http://www.m4art.org) has been (partly) founded on the techniques discussed in this thesis. References: - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2004). The utilization of human color categorization for content-based image retrieval. Proceedings of SPIE (Human Vision and Electronic Imaging), 5292, 351-362. [see also Chapter 7] - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2005). Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions. Journal of Imaging Science and Technology, 49(3), 293-301. [see also Chapter 8] - Broek, E.L. van den, Schouten, Th.E., and Kisters, P.M.F. (2008). Modeling Human Color Categorization. Pattern Recognition Letters, 29(8), 1136-1144. [see also Chapter 5] - Schouten, Th.E. and Broek, E.L. van den (2004). Fast Exact Euclidean Distance (FEED) transformation. In J. Kittler, M. Petrou, and M. Nixon (Eds.), Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 2004), Vol 3, p. 594-597. August 23-26, Cambridge - United Kingdom. [see also Appendix C

Content Based Image Retrieval based on Shape with Texture Features

In areas of state, domain and hospitals, massive collections of digital pictures are being created. These image collections are the merchandise of digitizing existing collections of analogue images, diagrams, drawings, paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image retrieval system is obtainable in this paper, for feature extraction HSV color space and wavelet transform approach are used. Initially constructed one dimension feature vector and represented the color feature it is made by that the color space is quantified in non-equal intervals. Then with the help of wavelet texture feature extraction is obtained. At last by using of wavelet transform combined the color feature and texture feature method. The illustration features are susceptible for different type images in image retrieval experiments. The color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At the same time, experiments reveal that HSV texture feature based on wavelet transform has better effective performance and stability than the RGB. The same work is performed for the RGB color space and their results are compared with the proposed system. The result shows that CBIR with the HSV color space is retrieves image with more accuracy and reduced retrieval time. Keywords--Content Based Image Retrieval, HSV, RG

Color image quality measures and retrieval

The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure. Three algorithms are designed for visual representation: (1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio. (2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI). (3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding. Three algorithms are designed for image quality measure (IQM): (1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain. (2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured. (3) A no-reference color IQM based upon colorfulness, contrast and sharpness

TEMU KEMBALI CITRA BATIK PESISIR

The title of this study was Retrieval Citra Batik Pesisir. The purpose of this study is how to measure the similarity between the query image to the image database on the image of a very diverse coastal batik influenced by the geographical location of the islands, the state of nature, and the structure of society, by implementing the transformation method curvelet fast discrete and HSV color space. And knowing the average yield precision image retrieval based coastal batik content using relative method manhatan. This research uses experimental analysis on coastal batik image dataset of the query image with the image in the database. Coastal batik image data taken are secondary data from the internet already results reproduk regardless of noise and shooting techniques. Applications are designed and built in stages as a test prototype image retrieval coastal batik using programming language Matlab R2014a. Based on the description of explanation and discussion on the overall chapter thesis for image retrieval batik to determine the extraction of the best features that the average value of precision curvelet scale of 4 to 85.17%, curvelet scale of 5 namely 88.62%, curvelet scale of 6 average precision value ie 90.73 %. For the average value of precision for HSV color space by 81.55%. As for the average value of precision curvelet scale 4 and HSV color space by 83.44%, to curvelet scale of 5 and HSV color space by 85.34% and to curvelet scale 6 and HSV color space by 87.71%. From this it can be deduced that the higher the measurement scale, the average precision is getting better at the time of image retrieval.Keywords: Content Based Image retrieval, Transformasi Curvelet Fast Discrete , Ruang Warna HSV , Relative Manhatan, Precision

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

Higher Image Retrieval Efficiency Using Color Features

Image retrieval techniques are useful in many image processing applications. Content based image retrieval systems works with whole images and searching is based on comparison of the query. In this paper we present a novel frame work for the color information and achieve higher retrieval efficiency using dominant color feature. An image is partitioned into sub blocks of equal size as a first step. Color of each sub block is extracted by using the RGB color space. A one to one matching scheme is used to compare the query and target images. Minkowski distance is used to retrieving the similar images. The experimental results demonstrate the efficiency of the method