Implementasi Dan Analisis Content-Based Image Retrieval Menggunakan Ekstraksi Warna Dan Sisi

ABSTRAKSI: Pada Tugas akhir ini, Content-based Image Retrieval yang dikembangkan yaitu berdasarkan fitur warna dan sisi. Fitur warna diekstraksi menggunakan metode Color Histogram, dimana metode ini menunjukkan tingkat intensitas warna atau graylevel dari sebuah citra. Color Histogram yang didapat dibandingkan antara citra query dan citra database dengan menggunakan Histogram Comparasion. Untuk ekstraksi fitur sisi, digunakan metode Edge Detection menggunakan mask matrik Prewitt yang dimodifikasi. Metode ini mencari fitur sisi dari citra, yang nantinya dibandingkan antara citra uji dan citra database. Dengan melakukan kombinasi kedua ekstraksi fitur, maka akan mengurangi proses perbandingan antar citra yang tidak relevan. Sehingga, citra-citra yang tidak memiliki kemiripan warna maupun sisi dieliminasi dengan tahap kombinasi ekstraksi ini. Untuk mengetahui pengaruh dari kombinasi kedua fitur ini, maka perlu dilakukan perbandingan terhadap masing-masing fitur. Dan berdasarkan hasil pengujian, dengan mengkombinasi fitur warna dan sisi ini memberikan hasil keakuratan yang lebih rendah yaitu 50% dibandingkan dengan masing-masing fitur secara terpisah, yaitu ekstraksi warna 55% dan ekstraksi sisi 46%.Kata Kunci : Content-based Image Retrieval, Color Histogram, Histogram Comparasion, Edge Detection, mask matrik PrewittABSTRACT: On this final project, developing Content-based Image Retrieval based on color and edge feature. Color feature is extracted using Color Histogram, where this method show color intensity level or graylevel from an image. Retrieved Color Histogram will be compared between query image and database image using Histogram Comparasion. To extracting edge feature, Edge Detection method with modified Prewitt mask matrix are used. This method will find image’s edge feature, which will compared between query image and database image. Using combination both feature extraction will be reduce some process such as comparing image which is not relevant. So that, images that haven’t similarity color and edge feature will be eliminated with combination filter. To find out the effect from combining this features, it’s necessary to comparing each feature. And based on experimental results, using combination of color and feature texture show the accuracy result lower which is 50% than using each feature separately, which is 55% for color extraction and 46% for edge extraction.Keyword: Content-based Image Retrieval, Color Histogram, Histogram Comparasion, Edge Detection, mask matrix Prewit

ANALISA PENERAPAN METODE MULTI-SCALE EDGE DETECTION DAN COLOR HISTOGRAM DALAM PROSES PENCARIAN GAMBAR

ABSTRAKSI: Content-based image retrival adalah pencarian gambar dengan memanfaatkan fitur ciri yang ada pada gambar. Fitur ciri tersebut dapat berupa bentuk, warna, tekstur, dan lain-lain. Ekstrasi ciri yang dibutuhkan untuk pencarian berbeda-beda tergantung dari domain gambar yang akan dicari. Dengan menggunakan ekstraksi ciri yang tepat, hasil pencarian dapat menjadi lebih baik. Untuk gambar dengan domain flora dan fauna, ekstraksi ciri yang dapat digunakan adalah ekstraksi sisi dan ekstraksi warna.Dari permasalahan yang dikemukakan di atas maka dibangunlah sebuah sistem image retrieval dengan memanfaatkan fitur warna dan fitur sisi dari citra. fitur warna citra diekstrak dengan menggunakan color histogram. Sedangkan fitur sisi dari citra akan diekstrak dengan menggunakan multi-scale edge detection. Dari sistem yang dibangun ini kemudian dilihat bagaimana performansi pencarian gambar yang dihasilkan serta faktor-faktor apa saja yang mempengaruhi hasil pencarian.Algoritma multi-scale edge detection adalah metode mencari representasi edge sebuah gambar dengan menggunakan sebuah operator edge detection akan tetapi proses pencarian edge dilakukan beberapa kali dengan perbedaan nilai Gaussian blur sehingga diperoleh hasil ekstraksi edge yang lebih baik. Color histogram yang digunakan dibagi berdasarkan color space dan representasi histogramnya. Dengan memanfaatkan edge detection dan color histogram diharapkan hasil pencarian citra bisa menjadi lebih baik.Dari hasil pengujian yang dilakukan, dengan menggabungkan ekstraksi sisi dan ekstraksi warna, performansi pencarian citra dapat ditingkatkan. Peningkatan ini dapat dilihat dari peningkatan nilai precision yang diperoleh. Besarnya nilai precision dipengaruhi oleh beberapa faktor, seperti komposisi warna citra dan warna background, detail edge yang dihasilkan, metode pengukuran jarak antara query dan database, dan bobot untuk similarity yang diberikan untuk ekstraksi ciri.Kata Kunci : image retrieval, ekstraksi ciri, multi-scale edge detection, color histogramABSTRACT: Content-based image retrieval is an image search using feature extraction of image characteristic. The Image characteristic is defined by shape, color, texture, and else. Feature extraction that required is different depends on the image domain. Using the right feature extraction of image could obtain a better result. For images which contain flora and fauna, the feature extraction that could be use is edge extraction and color extraction.From the problem that explained above, thus, is built an image retrieval system using color extraction and edge extraction from image. Color feature of image is extracted by using color histogram. Whereas, the edge feature of image is extracted by using multi-scale detection. Afterwards, from the system which built, the performance of image retrieval along with the factors that affect the image retrieval can be yielded.Multi-scale detection algorithm is a searching method to represent an edge of image using edge detection operator, however, edge detection process retrieved several time with the different value of Gaussian blur, so the better edge extraction is obtained. Color Histogram that used based on color space and histogram representation. By using edge detection and color histogram, the image retrieval is expected better.From a trial which had done, combining edge extraction and color extraction could enhance performance of image retrieval. This enhancement is perceived by the improvement of precision value. The precision value is affected by of several factors such composition of image color and background, edge detail, distance measurement between query and database, and weight of similarity that given for feature extraction.Keyword: image retrieval, feature extraction, multi-scale edge detection, color histogra

Image Retrieval Based on Fuzzy Edge and Trum Fuzzy Histogram

ABSTRACT In recent years, many image retrieval systems based on color feature like fuzzy color histogram, have been applied in image retrieval systems based on content (CBIR). Most of this methods are not able to determine pixels accurate colors, especially in combined manner, and only determine whole distribution of color factor in image; therefore they are not efficient in image retrieval. We have suggested weight vector factor in trum fuzzy histogram in this paper to remove these problems. But these methods only demonstrate total distribution of color feature in image and do not consider any kind of place data, like relative positions of objects in image. Therefore do not prepare strong techniques for image retrievals with complex place ornament. since the edge pixels are important places in image and determine objects in an image and often similar images have similar backgrounds, we use competitive fuzzy edge finder algorithm which effectively categorizes image pixels into 5 classes ,including 4 edge classes in different directions and 1 background class. after categorizing pixels, feature vector for each class would be determined, that includes Trum fuzzy color histogram and place position. we compared our suggested method to fuzzy histogram method and compound neighborhood fuzzy entropy method with color _place feature, as tests results show high efficiency of our suggested method for image retrievals from COREL database, including 3000 images

On the use of edge orientation and distance for content-based image retrieval

[[abstract]]Recently, various features for content-based image retrieval (CBIR) have been proposed, such as texture, color, shape, and spatial features. In this paper we propose a new feature, called orientation-distance histogram for CBIR. Firstly, we transform the RGB color model of a given image to the HSI color model and detect edge points by using the H-vector information. Secondly, we evaluate the orientation-distance histogram from the edge points to form a feature vector. After normalization of feature, our proposed method can cope with most problems of variations in image. Finally, we show some results of query for real life images with the precision and recall rates to measure the performance. The experimental results show that the proposed retrieval method is efficient and effective[[notice]]補正完畢[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20051013~20051015[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Beijing, Chin

Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010

Design of Video Retrieval System Using MPEG-7 Descriptors

AbstractThe paper proposes a content-based video retrieval system designed using MPEG-7 (multimedia content description interface), which provides a standard description for a video. The system consists of three parts: shot boundary detection, feature extraction and similarity measurement. In shot boundary detection, cut and dissolve can be detected using the histogram difference and skipping image difference, respectively. In feature extraction part, two MPEG-7 visual descriptors, Color Structure Descriptor (CSD) and Edge Histogram Descriptor (EHD), are used to represent the color feature and edge feature of the key frames. Lastly, the similarity between key frames is calculated using dynamic-weighted feature similarity calculation. The proposed system is tested on three kinds of videos. Promising results are obtained in terms of both effectiveness and efficiency

PENGELOMPOKAN GAMBAR BERDASARKAN WARNA DAN BENTUK MENGGUNAKAN FGKA (FAST GENETIC KMEANS ALGORITHM) UNTUK PENCOCOKAN GAMBAR

A large Collection of digital images in many areas of aspect are being created. The collection images are digitizing result of analogue photographs, diagrams, paintings, drawings, prints. Usually,the way of searching these collections was by indexing and image information based on text (like caption or keywords). This way is not effective and efficient because two reasons, are big size of database and subjective in picture meaning. Recently, it has been developed many ways in image retrieval that use image content (color, shape, and texture). The use of centroid produced from clustered RGB Histogram and Edge Detection matrix using FGKA, can be used for searching parameter. FGKA is merger of Genetic Algorithm and Kmeans Clustering Algorithm. FGKA is also developed from Genetic Kmeans Algorithm (GKA) which is always converge to global optimum. Image Clustering and Matching based on color-shape feature are better than based on color feature only if using some data wich are greatly into shape feature