Cut Desva Mutia

Bio: Cut Desva Mutia is an academic researcher from Syiah Kuala University. The author has contributed to research in topics: Image retrieval & Histogram of oriented gradients. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

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

Abstract: 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.

Skin segmentation based on improved thresholding method

Abstract: Segmentation aims at partitioning area in the image based on color, shape or textures. It is useful in many computer vision applications such as medical image analysis, object detection and recognition, and content-based image retrieval (CBIR). Currently, CBIR has been applied in an online clothing purchasing system. Here, the goal of the segmentation process is to segment the clothing parts from the background and the model skin. Practically, we want to detect the skin color and segment it from the image, and left the apparel parts in the image for matching processes. We propose an improved threshold-based segmentation technique by adding a new constraint to the previous method to limit the skin tone selection. The technique improved the segmentation results by correctly segment the apparels with the shade of purple, pale pink, and pinkish-yellow. Simulations on total 100 garment images showed that the proposed method increased the segmentation rate by 27%.

Application of content based image retrieval in digital image search system

Abstract: Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.

HOG of Region of Interest for Improving Clothing Retrieval Performance

Abstract: Due to spread of garment online store and the revenue from the online clothing market, clothing retrieval is becoming an active research topic in the last decade. The buyer commonly searches for shape, but the availability of various clothing designs and details on the Internet caused a growing interest to buy clothing with similar detail, at a particular position. The proposed method aims at enhancing the performance of garment image retrieval, in which the garments have similar details at a particular position. The method is developed based on histogram of oriented gradient (HOG), with a new approach for ROI partition mechanism prior employing the HOG. Simulations with four categories of clothing details confirmed that the proposed approach can improve the retrieval performance.

Cross-domain Clothing Image Retrieval Based on Hybrid Shape Feature

Abstract: Nowadays, Islamic woman garment attracts many users, indicated by the increasing amount of garment industry in many countries. Most of garment purchasing is done electronically. Keywords are inputted to the online shopping system to choose the expected garment model. However, it is very complicated to describe a garment model using keywords. Sketch-based image retrieval (SBIR) is an approach of retrieving a garment based on the content of the clothing. The objective of this paper is to enhance Pyramid Histogram of Oriented Gradient (PHOG) performance on SBIR for Islamic woman garment. This research combined the PHOG and Moore-Neighbour tracing (MNT) method for extracting a new feature. The oriented gradient of the image is calculated; the MNT was used to detect the boundary of the image. Then, the hybrid feature of the oriented gradient and the image boundary is created. The spatial pyramid is then generated from the feature. We refer the proposed method as pyramid histogram of oriented gradient cross-feature (PHOGXF). We conducted simulations on 450 image datasets, which consisted of three categories: blouse-pant, long-dress, and tunic. We did a cross-domain retrieval; we retrieved the clothing images in the database using a sketch image as the query. The result showed that the proposed method obtained the precision value of 73.5% for blouse-pant, 90.4% for long-dress, and 52.1% for the tunic, while the PHOG only achieved the precision value of 18.0% for blouse-pant, 65.4% for long-dress, and 21.0% for the tunic. We concluded that the proposed method (PHOGXF) has significant performance; more than twice as good as the PHOG method.

Skin segmentation based on improved thresholding method

Abstract: Mr.S Sreenivasulu, A suresh, K Venumadhav, D Bhavyakanth Reddy. Asso.Professor, Student, Student, Student. Electronics and Communication Engineering, Geethanjali Institute of Science and Technology, Nellore, Abstract: Segmentation aim at partitioning area in the image it will be based on the shape ,color or texture.It is useful in many computer vision applications such as medical image analyzing, object detection and recognizing, forensic applications.content based image retrieval is used on the online clothing purchasing.Here our goal is to segment the clothing parts from the image on the background and the skin area.We need to detect the skin color and segment the skin from the image,and left the apparel parts in the image for matching processes.Here We Propose an improved threshold-based segmentation technique by adding a new constraint to the previous method to limit the skin tone selection.The technique improved the segmentation results correctly segmenting the apparels with the shade of purple, pale pink, and pinkish-yellow.By the simulation of the images showed that the proposed method increased the segmentation results by 27%.