RISE: A ROBUST IMAGE SEARCH ENGINE

This thesis advances RISE (Robust Image Search Engine), an image database application designed to build and search an image repository. rise is built on the foundation of a CBIR (Content Based Image Retrieval) system. The basic goal of this system is to compute content similarity of images based on their color signatures. The color signature of an image is computed by systematically dividing the image into a number of small blocks and computing the average color of each block using ideas from DCT (Discrete Cosine Transform) that forms the basis for JPEG (Joint Photographic Experts Group) compression format. The average color extracted from each block is used to construct a tree structure and finally, the tree structure is compared with similar structures already stored in the database. During the query process, an image is given to the system as a query image and the system returns a set of images that have similar content or color distribution as the given image. The query image is processed to create its signature which is then matched against similar signature of images already stored in the database. The content similarity is measured by computing normalized Euclidean distance between the query image and the images already stored in the database. RISE has a GUI (Graphic User Interface) front end and a Java servlet in the back end that searches the images stored in the database and returns the results to the web browser. RISE enhances the performance of image operations of the system using JAI (Java Advance Imaging) tools

How successful firms go beyond aligning their IT strategy with business objectives

Thesis (S.M. in System Design and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 68-71).Information technology (IT) is vital to growth of all organizations. But getting value out of IT has been challenging. The companies, which fail to align their IT strategy with business objectives struggle with low or mediocre return on their IT investment. On the other hand, the companies that achieve strategic alignment realize higher economic benefits. Successful companies go one step further and use IT to enable business. They differentiate themselves from their competitors using IT and forge alliances. But is there any formula for achieving strategic alignment? The research of past decade seem to suggest that there indeed is a trend among companies, who manage to achieve strategic alignment. The successful companies recognize IT's unique value and ensure that it generates value like other assets do. IT is not a mere support function in such organizations. IT not only serves the internal businesses of the company but it acts like a business in dealing with suppliers. The framework of Strategic Alignment Model (SAM) identifies this as the balance of internal and external domain. The model asserts that IT should be judged both in terms of external domain, which determines how the firm as whole is positioned in the market place and internal domain, which constitutes IT's internal policies and structures. In the internal domain, the emphasis is more on technology than on business, management or organizational issue. The effective utilization of IT requires alignment of IT strategy with business objectives. This assertion is validated by a case study of a three companies, who successfully achieved strategic alignment.by Debangshu Goswami.S.M.in System Design and Managemen

RISE: A Robust Image Search Engine

In this article we address the problem of organizing images for effective and efficient retrieval in large image database systems. Specifically, we describe the design and architecture of RISE, a Robust Image Search Engine. RISE is designed to build and search an image repository, with an interface that allows for the query and maintenance of the database over the Internet using any browser. RISE is built on the foundation of a CBIR (Content Based Image Retrieval) system and computes the similarity of images using their color signatures. The signature of an image in the database is computed by systematically dividing the image into a set of small blocks of pixels and then computing the average color of each block. This is based on the Discrete Cosine Transform (DCT) that forms the basis for popular JPEG image file format. The average color in each pixel block forms the characters of our image description. Organizing these pixel blocks into a tree structure allows us to create the words or tokens for the image. Thus the tokens represent the spatial distribution of the color in the image. The tokens for each image in the database are first computed and stored in a relational database as their signatures. Using a commercial relational database system (RDBMS) to store and query signatures of images improves the efficiency of the system. A query image provided by a user is first parsed to build the tokens which are then compared with the tokens for images in the database. During the query process, tokenization improves the efficiency by quantifying the degree of match between the query image and images in the database. The content similarity is measured by computing normalized Euclidean distance between corresponding tokens in query and stored images where correspondence is defined by the relative location of those tokens. The location of pixel blocks is maintained by using a quad tree structure that also improves performance by early pruning of search space. The distance is computed in perceptual color space, specifically L * a * b * and at different levels of detail. The perceptual color space allows RISE to ignore small variations in color while different levels of detail allow it to select a set of images for further exploration, or discard a set altogether. RISE only compares the precomputed color signature images that are stored in an RDBMS. It is very efficient since there is no need to extract complete information for every image. RISE is implemented using object-oriented design techniques and is deployed as a web browser-based search engine. RISE has a GUI (Graphical User Interface) front-end and a Java servlet in the back-end that searches the images stored in the database and returns the results to the web browser. RISE enhances the performance of image operations of the system by using JAI (Java Advance Imaging) tools, which obviates the dependence on a single image file format. In addition, the use of RDBMS and Java also facilitates the portability of 1 2 Goswami, Bhatia, Samal the system

Implementation of pediatric allergic rhinitis module as a part of AETCOM among first-year medical undergraduates: Mixed methods evaluation

Background: Children suffering from allergic rhinitis (AR) in their earlier days of life, not receiving proper treatment, subsequently develop asthma. To sensitize the first-year medical undergraduates about AR by implementing pediatric allergic rhinitis (PAR) module as a part of their attitude, ethics, and communication (AETCOM) curriculum. Materials and Methods: Triangulation type of mixed method study was conducted from January 2021 to June 2021 among 125 first-year medical undergraduate students. The PAR module communication checklist was developed and validated by an interprofessional (IP) team. Twenty multiple-choice questions (MCQs) were framed for both pretest and posttest cognitive assessment of the students. The pretest assessment was done (first 15 min) followed by the teaching of the PAR module (30 min), and lastly the posttest assessment along with open-ended feedback (last 15 min). Objective Structured Clinical Examination (OSCE) communication checklist along with the guidelines was given to the observer during the student-patient encounter to score the learner and to assess the communication skill. Apart from descriptive analysis, paired t-test and content analysis were done. Results: A statistically significant difference in the mean scores before and after the PAR module and communication checklist (P < 0.001). Majority (78/81, 96%) of the students favored this module, while (28/81) 34.6% suggested modifications. Most of the parent's feedback was good about the student's communication skill in terms of empathy (118), behavior (107), and greet (125); however, 33 parents were about the opinion of difficulties in closing the session, 17 parents commented about student's language problem and 27 about feedback. Conclusion: The PAR module should be taught in the current medical curriculum as a part of AETCOM in the foundation course as early clinical exposure with some modifications in the existing module