Effectiveness of MPEG-7 Color Features in Clothing Retrieval

Clothing is a human used to cover the body. Clothing consist of dress, pants, skirts, and others. Clothing usually consists of various colors or a combination of several colors. Colors become one of the important reference used by humans in determining or looking for clothing according to their wishes. Color is one of the features that fit the human vision. Content Based Image Retrieval (CBIR) is a technique in Image Retrieval that give index to an image based on the characteristics contained in image such as color, shape, and texture. CBIR can make it easier to find something because it helps the grouping process on image based on its characteristic. In this case CBIR is used for the searching process of Muslim fashion based on the color features. The color used in this research is the color descriptor MPEG-7 which is Scalable Color Descriptor (SCD) and Dominant Color Descriptor (DCD). The SCD color feature displays the overall color proportion of the image, while the DCD displays the most dominant color in the image. For each image of Muslim women\u27s clothing, the extraction process utilize SCD and DCD. This study used 150 images of Muslim women\u27s clothing as a dataset consistingclass of red, blue, yellow, green and brown. Each class consists of 30 images. The similarity between the image features is measured using the eucludian distance. This study used human perception in viewing the color of clothing.The effectiveness is calculated for the color features of SCD and DCD adjusted to the human subjective similarity. Based on the simulation of effectiveness DCD result system gives higher value than SCD

Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG

The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories

A Support Vector Machine (SVM) and Speeded Up Robust Features (SURF) for Indonesian Car Licence Plate Identification System

Volume 7 Issue 10 (October 201

Metode Band-Limited Phase Only Correlation (BLPOC) Untuk Identifikasi Plat Kendaraan

Digital image processing and computer vision technologies have developed so rapidly and have numerous applications. Automatic lisence plate recognition systems (ALPRS) based on those technologies are not exceptions. In general, the ALPRSs required several steps including image capturing, plate location searching, character segmentation and character recognition. Successful of the whole systems depended heavily on the used segmentation method. A common drawback of many segmentation techniques is that they are very sensitive to illumination variability. The paper proposed a method for license plate recognition based on correlation of phase componenet with limited bandwidth. The method is widely known as band-limited phase only correlation (BLPOC). The method compared input plate\u27s image with plate\u27s images in the database. Based on simulation, detection rate can achieve 90% if an appropriate threshold value was selected

Peningkatan Kualitas Citra Digital Menggunakan Metode Super Resolusi Pada Domain Spasial

Citra merupakan salah satu komponen dari multimedia yang memegang peranan penting karena mengandung informasi dalam bentuk visual. Tetapi tidak semua citra dapat menampilkan informasi secara jelas dan detail seperti halnya citra resolusi rendah. Citra resolusi rendah memiliki kepadatan piksel yang rendah. Untuk itudiperlukan peningkatan kualitas citra menggunakan metode super resolusi agar dihasilkan citra resolusi tinggi dari citra resolusi rendah. Penelitian ini menggunakan teknik super resolusi yang melalui tiga tahapan umum yaitu registrasi, interpolasi (bilinear dan bikubik), dan rekonstruksi (smoothing dan denoising). Teknik ini diterapkan pada domain spasial menggunakan citra grayscale resolusi rendah. Teknik super resolusi ini diaplikasikan pada citra satu framedan citra multiframe. Hasil Mean Opinion Score (MOS)menunjukkan bahwa citra multiframe yang melalui prosesregistrasi menghasilkan citra resolusi tinggi yang lebih baikdibandingkan dengan citra satu frame tanpa proses registrasi

Pengenalan Karakter Plat Nomor Kendaraan Bermotor Menggunakan Zoning dan Fitur Freeman Chain Code

A license plate is one of the vehicle identities. It consists of alphabetic characters and numbers and represents provincial and area code where the vehicle is registered. This article discusses the character recognition of plate number using zoning and Freeman Chain Code (FCC). Zoning divides character image into several zones i.e. 4, 6, and 8, and then, the pattern of each character in the zone is extracted using FCC as the numerical features. The character is then classified using Support Vector Machines (SVM). It is a multi-class classification problem with 36 categories. The results show that FCC features with 8 zones give the best accuracy (87%) when compared to the other two zones

Studi Pencocokan Plat Kendaraan Dengan Metode Phase Only Correlation

Salah satu cara pengenalan kendaraan adalah dengan identifikasi plat. Umumnya identifikasi yang dilakukan mengacu pada proses segmentasi tiap karakter dari citra plat. Makalah ini mengajukan suatu metode identifikasi plat yang sederhana tanpa melakukan pengenalan melainkan langsung pada proses pencocokan yang berbasis Phase Only Correlation (POC). POC mencocokkan plat dengan mengorelasikan fasa dari dua citra plat. Fasa diperoleh dengan mengubah citra dari domain spasial menjadi domain frekuensi menggunakan Transformasi Fourier Waktu Diskrit (TFWD). Nilai puncak POC akan tinggi jika citra plat yang dicocokkan adalah citra yang berasal dari plat yang sama. Sebaliknya akan rendah jika yang dicocokkan berasal dari plat yang berbeda. Hasil simulasi menggunakan 20 citra plat menunjukkan bahwa metode POC dapat digunakan dalam pencocokan citra plat

Ordinal Measure of Discrete Cosine Transform Blocks for Iris Identification

Currently, a common method for identifying a person is by means of an identitycard (ID) or combination of an ID and password. The approaches are not very reliable, since the ID can be stolen and password can be forgotten. A more reliable identification system is required. In the last decades, identification systems based on biometrics have been gaining attention, since they are more reliable. Biometrics-based devices identify people based on their physical or psychological characteristics, such as palmprints, fingerprints, gait and iris. Unlike fingerprints or palmprints, irides features distribute randomly, and the features were unique; the features between right and left eyes aredifferent, as well as between twins. Therefore, in addition to reliability, the use of irides can enhance identification accuracy. Purpose of the paper was to improve identification rate of an iris identification method, using ordinal measure of Discrete Cosine Transform (DCT) coefficient. The input iris image was tiled into blocks of 8x8 pixels, then the DCT was applied to each blocks. The AC coefficients of each block were sorted from the smallest to the largest values, in which the sorted values were referred to as ordinal measures.Identification was accomplished by measuring a distance between the ordinal measure of the input images with the ones of the existing images in the database using Minkwoski distance metric. Proposed method increased the averaged identification rate as compared to the previous method by nearly twice from 33% to 61.4%