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

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

Efficient Relay Selection Algorithm for Non-Orthogonal Amplify-and-Forward Cooperative Systems over Block-Fading Channels

In this paper, an efficient relay selection (RS) algorithm for non-orthogonal amplify-and-forward (NAF) cooperative systems over block-fading channels, also known as block-fading NAF (BFNAF) protocol, is developed. The best relay is selected from a subset of available relay nodes based on the maximum criterion of their capacity bounds in half-duplex (HD) mode, together with the power allocation, to obtain the energy efficiency (EE) for the proposed RS scheme. We derived an exact closed-form expression of the outage probability and throughput for evaluating the system performance. The energy consumption was also numerically evaluated to determine the optimized EE of the proposed RS scheme for each transmission protocol with two modulation schemes. The numerical results indicated that the proposed RS scheme with the BFNAF protocol outperforms the previous RS scheme with orthogonal AF (OAF) protocol in terms of both the outage probability and the throughput as the number of relays is increased and the average transmit power is optimally allocated for each transmission phase. Moreover, in the case of the optimized EE, it is found that by using quadrature amplitude modulation (QAM), the EE of the proposed RS scheme is 48.9% higher than that of binary phase-shift keying (BPSK) modulation

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

© 2013 IEEE. Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance

Linear and nonlinear techniques for multibeam joint processing in satellite communications

Existing satellite communication standards such as DVB-S2 operate under highly-efficient adaptive coding and modulation schemes thus making significant progress in improving the spectral efficiencies of digital satellite broadcast systems. However, the constantly increasing demand for broadband and interactive satellite links emanates the need to apply novel interference mitigation techniques, striving towards Terabit throughput. In this direction, the objective of the present contribution is to investigate joint multiuser processing techniques for multibeam satellite systems. In the forward link, the performance of linear precoding is investigated with optimal non-linear precoding (i.e. Dirty Paper Coding) acting as the upper performance limit. To this end, the resulting power and precoder design problems are approached through optimization methods. Similarly, in the return link the concept of linear filtering (i.e. Linear Minimum Mean Square Error) is studied with the optimal successive interference cancellation acting as the performance limit. The derived capacity curves for both scenarios are compared to conventional satellite systems where beams are processed independently and interbeam interference is mitigated through a four color frequency reuse scheme, in order to quantify the potential gain of the proposed techniques