A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

What medicines do households keep in their cabinets? Understanding the possession and use of medicines at home and the role of health insurance in Nigeria

Background Globally, the possession of medicines stored at home is increasing. However, little is known about the determinants of possessing medicines, their usage according to clinical purpose, which we term ‘correct drug match’, and the role of health insurance. Methods This study uses data from a 2013 survey evaluating a health insurance program in Kwara State, Nigeria, which upgraded health facilities and subsidized insurance premiums. The final dataset includes 1,090 households and 4,641 individuals. Multilevel mixed-effects logistic regressions were conducted at both the individual level and at the level of the medicines kept in respondents’ homes to understand the determinants of medicine possession and correct drug match, respectively, and to investigate the effect of health insurance on both. Results A total of 9,266 medicines were classified with 61.2% correct match according to self-reported use, 11.9% incorrect match and 26.9% indeterminate. Most medicines (73.0%) were obtained from patent proprietary medicine vendors (PPMVs). At 36.6%, analgesics were the most common medicine held at home, while anti-malarial use had the highest correct match at 96.1%. Antihistamines, vitamins and minerals, expectorants, and antibiotics were most likely to have an incorrect match at respectively 35.8%, 33.6%, 31.9%, and 26.6%. Medicines were less likely to have a correct match when found with the uneducated and obtained from public facilities. Enrolment in the insurance program increased correct matches for specific medicines, notably antihypertensives and antibiotics (odds ratio: 25.15 and 3.60, respectively). Conclusion Since PPMVs serve as both the most popular and better channel compared to the public sector to obtain medicines, we recommend that policymakers strengthen their focus on these vendors to educate communities on medicine types and their correct use. Health insurance programs that provide affordable access to improved-quality health facilities represent another important avenue for reducing the burden of incorrect drug use. This appears increasingly important in view of the global rise in antimicrobial resistance.

Modelling the expected probability of correct assignment under uncertainty

When making important decisions such as choosing health insurance or a school, people are often uncertain what levels of attributes will suit their true preference. After choice, they might realize that their uncertainty resulted in a mismatch: choosing a sub-optimal alternative, while another available alternative better matches their needs. We study here the overall impact, from a central planner’s perspective, of decisions under such uncertainty. We use the representation of Voronoi tessellations to locate all individuals and alternatives in an attribute space. We provide an expression for the probability of correct match, and calculate, analytically and numerically, the average percentage of matches. We test dependence on the level of uncertainty and location. We find that the overall mismatch is considerable even for low uncertainty—a possible concern for policy makers. We further explore a commonly used practice—allocating service representatives to assist individuals’ decisions. We show that within a given budget and uncertainty level, the effective allocation is for individuals who are close to the boundary between several Voronoi cells, but are not right on the boundary.

An Efficient Approach for Secure Storage, Search using AES in Cloud Storage

The expanded rate of development and adaption of distributed computing, day by day, more delicate data is being brought together onto the cloud. For the security of profitable data, the information must be encoded before externalization. The current inquiry procedures enable the client to seek over encoded information utilizing keyword yet these methods represent just correct hunt. There is no resilience for grammatical errors and organization irregularities which are typical client conduct. This makes compelling information stockpiling and use an exceptionally difficult errand, providing client seeking extremely baffling and wasteful. In this paper, we concentrate on secure capacity utilizing Advanced Encryption Standard (AES) and data recovery by performing Fuzzysearch on information which is scrambled while transferring on the cloud. We are proposing the execution of a progressed Fuzzykeywordtechnique called the Wildcard based procedures which restores the coordinating records when clients seeking inputs definitely coordinate the predefined keyword or the nearest conceivable coordinating documents in light of likeness catchphrase semantics when correct match comes up short. In the proposed method, using alter separation to measure keywords similitude and build up a proficient strategies for developing fuzzy keyword sets, which concentrate on diminishing the capacity and portrayal overtop.

Alphabet Recognition Based on Computer Vision

In order to realize rapid alphabet recognition, the paper proposes an alphabet recognition method based on computer vision optimization technical which can also extract the classification features. Experimental results show that the obtained variance value of the test image and the standard image obtained by the proposed method is the minimum which indicating the method can achieve correct match, effective classification, and provide a great method of identification.

Computerized brain tumor segmentation in magnetic resonance imaging

OBJECTIVE: To propose an automatic brain tumor segmentation system. METHODS: The system used texture characteristics as its main source of information for segmentation. RESULTS: The mean correct match was 94% of correspondence between the segmented areas and ground truth. CONCLUSION: Final results showed that the proposed system was able to find and delimit tumor areas without requiring any user interaction.

Image Registration of CT and DR Based on Improved SUSAN

The paper completes image registration of medical CT and DR through improved SUSAN algorithm. First, the paper divides the image into a certain number of anatomical point regions with some significance, and sets different grayscale D-value for different regions to make corners distribute equably. Second, the paper uses the size of the USAN from limited corner regions to reduce the number of corners and the computation. Then the paper adopts confidence algorithm, find the correct match points by setting threshold, and thus completes image registration.

Application of Digital Imaging Techniques in Detection of Defects in Thin Plywood

The detection of flaws and cavities in thin plywood boards saves money for manufactures of a variety of products. Flaws in the boundaries of pieces cut from plywood makes them useless. Therefore, it is essential to detect and locate knots and flaws in plywood boards before the cutting process. A detection and locating system was developed to detect knots and cavities in thin plywood boards using Digital Image Processing and light enhancement methods. The system comprises of three major components: a light source, a digital camera and a computer. The intense light source is used to brighten and reveal flaws and defects in the plywood board in an apparatus. The digital camera captures a digitized picture of the lighted board and stores it on the computer. Finally, a program written in Matlab™ code analyzes the captured image of the board, compares it to a template, and indicates whether flaws are located on the template’s cut lines. The advantage of using these methods is that it allows for the examination and analysis of the plywood without compromising its integrity. When a flaw is detected, the system repositions the plywood image in search of finding an orientation that will allow all defects to avoid cut lines. The process is repeated against several templates until the correct match is found. Once the match and usable orientation is found, a prompt will appear on the computer screen telling the system operator the template name and the orientation of the plywood board.

A multivariate distribution for sub-images

A new method for obtaining multivariate distributions for sub-images of natural images is described. The information in each sub-image is summarized by a measurement vector in a measurement space. The dimension of the measurement space is reduced by applying a random projection to the truncated output of the discrete cosine transforms of the sub-images. The measurement space is then reparametrized, such that a Gaussian distribution is a good model for the measurement vectors in the reparametrized space. An Ornstein–Uhlenbeck process, associated with the Gaussian distribution, is used to model the differences between measurement vectors obtained from matching sub-images. The probability of a false alarm and the probability of accepting a correct match are calculated. The accuracy of the resulting statistical model for matching sub-images is tested using images from the Middlebury stereo database with promising results. In particular, if the probability of accepting a correct match is relatively large, then there is good agreement between the calculated and the experimental probabilities of obtaining a unique match that is also a correct match.