Feature Vector Of Query Image Research Articles

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.

Preoperative Computer Simulation in Rhinoplasty Using Previous Postoperative Images.

Importance: Providing realistic and useful preoperative counseling based on a surgeon's knowledge and experience. Objective: Using previous preoperative and postoperative patients' images to predict the postoperative result of a new query patient. Design, Setting, and Participants: After preprocessing for image standardization, facial landmarking was done using 68 points on the frontal view and 19 points on the profile view. Facial features were calculated and image retrieval was done based on similarity measurement between the query image's feature vector and database images' feature vectors. The nasal areas on the postoperative retrieved images were swapped to the corresponding region on the query patient's face at a unicenter tertiary hospital. We randomly selected the color profile photographs of 400 patients (360 women and 40 men) from the database of all rhinoplasty patients who had been successfully operated at Valiasr hospital from 2010 to 2018. Results: The accuracy of this preoperative simulation was >80% in our pilot study in 20 patients. Conclusions and Relevance: This system is fast, easy to handle, reliable, and accurate to simulate postoperative outcomes both in frontal and profile views. We believe that this system could not only improve more informative communication between surgeons and patients but also could facilitate the training of surgeons and residents.

Performance Analysis of Distance Metric for Content Based Image Retrieval

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.

Architecture for Software as a Service (SaaS) Model of CBIR on Hybrid Cloud of Microsoft Azure

In various areas of government, academia, hospitals and commerce large collections of digital images are produced. Many of these collections are the merchandise of digitizing existing collections of analogue drawings, photographs, paintings and prints. Generally the only way of searching these collections was by basically browsing or keyword indexing. Digital images databases however, open the way to content-based searching. Content Based Image Retrieval (CBIR) is concerned with the retrieval of images similar to a specified image, from an image repository. Content Based Image Retrieval (CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image.This paper proposes a system that can be used for retrieving images related to a query image from a large set of distinct images. It follows an image segmentation based approach to extract the different features present in an image. The above features which can be stored in vectors called feature vectors and therefore these are compared to the feature vectors of query image and the image information is sorted in decreasing order of similarity. The processing of the same is done on cloud. The CBIR system is an application built on Windows Azure platform. It is a parallel processing problem where a large set of images have to be operated upon to rank them based on a similarity to a provided query image by the user. Numerous instances of the algorithm run on the virtual machines provided in the Microsoft data centers, which run Windows Azure. Windows Azure Stack is the operating system for the cloud by Microsoft Incorporation. Windows azure Stack is responsible for creating ideal hybrid architecture

Content Based Image Retrieval Using Gray Scale Weighted Average Method

High feature vector dimension quietly remained a curse element for Content Based Image Retrieval (CBIR) system which eventually degrades its efficiency while indexing similar images from database. This paper proposes CBIR system using Gray Scale Weighted Average technique for reducing the feature vector dimension. The proposed method is more suitable for color and texture image feature analysis as compared to color weighted average method as illustrated in literature review. To prove the effectiveness of retrieval system, two standard benchmark dataset namely, Wang and Amsterdam Library of Texture Images (A LOT) for color and texture have been selected to evaluate the system retrieval accuracies as well as efficiencies generated by each method. For the purpose of image similarity, Euclidean distance has been employed which matches query image feature vector with image database feature vectors. The experimental results generated by two methods showed that overall performance of the proposed method is relatively better in terms of average precision, average recall and its average retrieval time.