52,673 research outputs found

    Building an Efficient Content Based Image Retrieval System by Changing the Database Structure

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    Content Based Image Retrieval (CBIR) is still a major research area due to its complexity and the growth of the image databases. Color Based Image Retrieval is one of the major retrieval methods in Content Based Image Retrieval systems. At present, researchers combine image retrieval techniques to get more accurate results. With the large image databases, image retrieval is still a challenging area and the efficiency of the image retrieval techniques still need to be considered. For this purpose, a comparative study of image retrieval techniques has been discussed in this paper. In addition, an efficient method is presented which aids to retrieve images by storing an intermediate result of the process in the database. To compare the query image and the images in the database, Euclidean distance, Normalized Cross Correlation distance and Histogram Intersection distance are taken as distance measures. Experimental results demonstrate Histogram Intersection distance is better than the other two methods. The intermediate result was stored using an event in the system. By making minor modifications to the proposed system, it creates a possibility for the user to add images to the database just by clicking on a button. Thus, the user can expand his/her database on his/her own will. Results show a significant improvement of performance in the proposed method

    Human-Centered Content-Based Image Retrieval

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    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

    Cuckoo Search Algorithm Based Feature Selection in Image Retrieval System

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    Efficiency an d retrieval time are very important issues in any content-based image retrieval system. In this study, an efficient image retrieval system was introduced depending on several features extracted from the database images, namely color moment (Mean, Standard Deviation), GLCM, and DWT( only LL-sub band). To increase the retrieval speed, Cuckoo search algorithm was used to select the important positions that contain full power features from the (LL-sub band). On using the Cuckoo search algorithm, only (50) important positions were chosen out of the total (24576) positions within (LL- sub band). These positions were stored for later use when entering a query image. Thus, the time taken to retrieve images was greatly reduced and this process also increased the efficiency of the system due to the fact that the selected positions gave the lowest distance measures between the query images and the similar images when evaluated using Manhattan distance measure. Two effectual performance measures (precision & recall) were used to calculate the accuracy of the system. The findings proved the system efficiency when compared to other previous works. Keywords: CBIR, Color Moment, GLCM, DWT, Cuckoo Search algorithm, Manhattan measure DOI: 10.7176/JEP/10-15-08 Publication date:May 31st 201

    Analisis Dan Implementasi Content Based Image Retrieval Berdasarkan Ciri Warna, Tekstur dan Bentuk

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    ABSTRAKSI: Kebutuhan akan sistem yang mampu melakukan pencarian image sesuai keinginan user dan seiring dengan terus berkembangnya ukuran basis data image membuat metode pencarian image dengan kata kunci berupa teks tidak lagi bisa diandalkan karena kata kunci yang diinputkan mungkin tidak sesuai dengan image yang diharapkan sehingga dengan memberikan kata kunci saja tidak cukup. Hal ini disebabkan pemberian nama image bisa bersifat tidak objektif.Dari permasalahan tersebut dibangunlah sistem yaitu Content Based Image Retrieval (CBIR) dengan menerapkan ekstraksi ciri warna (Color Moments), ciri tekstur (Haar Wavelet) dan ciri bentuk (Centroid Contour Distance) untuk mendapatkan image yang sesuai dengan image yang dicari. Ekstraksi fitur warna menggunakan Color Moments memanfaatkan distribusi probabilitas warna sebuah image yang terdiri dari 3 moments yaitu : mean, standard deviation, dan skewness. Sedangkan untuk ekstraksi tekstur dengan melakukan dekomposisi Haar Wavelet dengan pendekatan pyramid-structured wavelet transform (PWT) yang menghasilkan 12 fitur ciri. Untuk ekstraksi bentuk menggunakan Centroid Contour Distance dengan menghitung jarak dari centroid ke tepi objek dengan menggunakan perhitungan sudut 50 dan menghasilkan 72 fitur ciri.Hasil penelitian menunjukkan bahwa penggabungan ketiga metode secara paralel menghasilkan akurasi dan performansi yang lebih baik serta menghasilkan peningkatan nilai F-Measure dari pada penggabungan secara serial maupun individu. Akurasi yang diperoleh 96.39% dan performansi 43.77% serta selisih nilai F-Measure 19.65%.Kata Kunci : content based image retrieval, color moments, haar wavelet, centroid contour distance.ABSTRACT: Along with the continued development of image database making searching method a image based on keyword is not enough. Beacause, name of image can be given not objective.These problems built Content Based Image Retrieval based on color features extraction (Color Moments), texture features (Haar Wavelet) and shape features (Centroid Contour Distance) to obtained an appropriate with image query. Extraction of color features with Color Moments which is use a color probability distributions image that consist of 3 moments : mean, standar deviation and skewness.To extracting texture used decomposition Haar Wavelet approach pyramid-structured wavelet transform (PWT) which produces 12 feature texture. For shape extraction with Centroid Contour Distance calculating the distance from centroid to the edge of object. Calculating used angle 50 dan produces 72 shape feature.The result showed that paralel combination of 3 method has the accuracy and perfromance result better also increased value of F-Measures than serial combination or without combination. Accuracy obtained 96.39% and performance 43.77% with difference value of F-Measures 19.65%.Keyword: content based image retrieval, color moments, haar wavelet, centroid contour distance

    A NEW HCL COLOR SPACE WITH ASSOCIATED COLOR SIMILARITY MEASURE FOR COLOR-BASED IMAGE RETRIEVAL

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    Color analysis is frequently used in image/video retrieval. However, many existing color spaces and color distances fail to capture correctly color differences usually perceived by the human eye. The objective of this paper is first to highlight the limitations of existing color spaces and similarity measures in representing human perception of colors, and then to propose (i) a new perceptual color space model called HCL, and (ii) an associated color similarity measure denoted DHCL. Experimental results show that using DHCL on the new color space leads to a solution very close to human perception of colors and hence to a potentially more effective content-based image/video retrieval. Moreover, the application of the similarity measure DHCL to other spaces like HSV leads to a better retrieval effectiveness. A comparison of HCL against L*C*H and CIECAM02 spaces using color histograms and a similarity distance based on Dirichlet distribution illustrates the good performance of HCL for a collection of 3500 images of different kinds.Key words : HCL color space, color analysi

    Semi-automated techniques for the retrieval of dermatological condition in color skin images

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    Dermatologists base the diagnosis of skin disease on the visual assessment of the skin. This fact shows that correct diagnosis is highly dependent on the observer\u27s experience and on his or her visual perception. Moreover, the human vision system lacks accuracy, reproducibility, and quantification in the way it gathers information from an image. So, there is a great need for computer-aided diagnosis. We propose a content-based image retrieval (CBIR) system to aid in the diagnosis of skin disease. First, after examining the skin images, pre-processing will be performed. Second, we examine the visual features for skin disease classified in the database and select color, texture and shape for characterization of a certain skin disease. Third, feature extraction techniques for each visual feature are investigated respectively. Fourth, similarity measures based on the extracted features will be discussed. Last, after discussing single feature performance, a distance metric combination scheme will be explored. The experimental data set is divided into two parts: developmental data set used as an image library and an unlabeled independent test data set. Two sets of experiments are performed: the input image of the skin image retrieval algorithm is either from developmental data set or independent test data set. The results are top five candidates of the input query image, that is, five labeled images from image library. Results are laid out separately for developmental data set and independent test data set. Two evaluation systems, both the standard precision vs. recall method, and the self-developed scoring method are carried out. The evaluation results obtained by both methods are given for each class of disease. Among all visual features, we found the color feature played a dominating role in distinguishing different types of skin disease. Among all classes of images, the class with best feature consistency gained the best retrieval accuracy based on the evaluation result. For future research we recommend further work in image collection protocol, color balancing, combining the feature metrics, improving texture characterization and incorporating semantic assistance in the retrieved process

    Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

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    We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure
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