In many areas of commerce, government, academia, and hospitals, large collections of digital im-
ages are being created. Many of these collections are the product of digitizing existing collections of
analogue photographs, diagrams, drawings, paintings, and prints. Usually, the only way of search-
ing these collections was by keyword indexing, or simply by browsing. Digital images databases
however, open the way to content-based searching. In this paper we survey some technical aspects
of current content-based image retrieval systems.

Content-based image retrieval systems: A survey

Acting Under Secretary of Health requested that a working group be established to develop the Veterans Health Initiative (VHI). He envisioned this as a comprehensive program to recognize the connection between certain health effects and military service, to allow military history to be better documented, to prepare health care providers to better serve their veteran patients, and to establish a data base for further study. This was first discussed by the Acting Under Secretary in relation to the health of former prisoners of war. Development was really begun by the former Chief Academic Affairs Officer, Dr. David Stevens, with the Military Service History project. This involves a pocket card for medical residents detailing the important components of a military service history targeting the health risks associated with various periods of service and more generic issues of concern and a website containing references relevant to the issues. Educational modules in the Veterans Health Initiative VHA will assist health care providers in recognizing the connection between certain health effects and military service, prepare health care providers to better serve veteran patients, and will provide a data base for further study. This independent study is designed to provide primary care practitioners with an introduction to the pathologies that lead to sight loss, their functional implications, appropriate method of referrals, training programs, and special considerations for interactions with visually impaired individuals. After completing this independent study, participants would be able to: • Define legal blindness; • Describe the causes of sight loss; • Delineate the functional implications of vision loss • Delineate the psycho/social impact of vision loss on the veteran; • Outline the role of the Visual Impairment Services Team (VIST) in the treatment of legally blind veterans and the referral process; • Describe the special personal and environmental considerations needed for visually impaired patients; • Describe the special medical considerations needed for visually impaired patients; • Describe the primary care practitioner's role in assisting veterans in establishing well-grounded claims for disability related to the loss of vision; and • Describe compensation and pension benefits provided for veterans with eye disabilities. After completing this independent study, you should 1. be able to: state the definition of legal blindness; 2. be able to: associate eye diseases with their visual implications; 3. be able to: demonstrate insight into the functional and Psycho/Social implications of sight-loss; 4. know when referrals to VIST are indicated; 5. understand …

시력 손상과 시각 장애(Visual Impairment and Blindness)

Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive.

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An analytical study of information extraction from unstructured and multidimensional big data

Various content-based image retrieval techniques are available for retrieving the require and classify images, we are reviewing them. In our first section, we are tending towards some basics of a particular CBIR system with that we have shown some basic features of any image, these are like shape, texture, color and shown different techniques to calculate them. In the next section, we have shown different distance measuring techniques used for similarity measurement of any image and also discussed indexing techniques. Finally conclusion and future scope is discussed.

A Survey on Content Based Image Retrieval Systems

Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R-CNN, SPPnet, Fast R-CNN, Faster R-CNN, Mask RCN, RFCN, FPN, 2) classification/regression base object detection methods such as YOLO(v2 to v5), SSD, DSSD, RetinaNet, RefineDet, CornerNet, EfficientDet, M2Det 3) Some latest detectors such as, relation network for object detection, DCN v2, NAS FPN. Moreover, five publicly available benchmark datasets and their standard evaluation metrics are also discussed. We mainly focus on the application of deep learning architectures to five major applications, namely Object Detection in Surveillance, Military, Transportation, Medical, and Daily Life. In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational power. Finally, we finish the survey by identifying fifteen current trends and promising direction for future research.

/pdf/exploring-deep-learning-based-architecture-strategies-4m33u38emf.pdf

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Content Based Image Retrieval (CBIR) is a very important research area in the field of image processing, and comprises of low level feature extraction such as color, texture and shape and similarity measures for the comparison of images. Recently, the research focus in CBIR has been in reducing the semantic gap, between the low level visual features and the high level image semantics. This paper provides a comprehensive survey of all these aspects. This survey covers approaches used for extracting low level features; various distance measures for measuring the similarity of images, the mechanisms for reducing the semantic gap and about invariant image retrieval. In addition to these, various data sets used in CBIR and the performance measures, are also addressed. Finally, future research directions are also suggested. (I. Felci Rajam, S. Valli. A Survey on Content Based Image Retrieval. Life Sci J 2013; 10(2): 2475-2487). (ISSN: 1097-8135). http://www.lifesciencesite.com 343

/pdf/a-survey-on-content-based-image-retrieval-2u15xt47nx.pdf

A Survey on Content Based Image Retrieval

This paper illustrates the overview of the sharedtask on automatic speech recognition in the Tamillanguage. In the shared task, spontaneousTamil speech data gathered from elderly andtransgender people was given for recognitionand evaluation. These utterances were collected from people when they communicatedin the public locations such as hospitals, markets, vegetable shop, etc. The speech corpusincludes utterances of male, female, and transgender and was split into training and testingdata. The given task was evaluated using WER(Word Error Rate). The participants used thetransformer-based model for automatic speechrecognition. Different results using differentpre-trained transformer models are discussedin this overview paper.

Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil

An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform ☆

- The existing information retrieval techniques do not consider the context of the keywords present in the user’s queries. Therefore, the search engines sometimes do not provide sufficient information to the users. New methods based on the semantics of user keywords must be developed to search in the vast web space without incurring loss of information. The semantic based information retrieval techniques need to understand the meaning of the concepts in the user queries. This will improve the precision-recall of the search results. Therefore, this approach focuses on the concept based semantic information retrieval. This work is based on Word sense disambiguation, thesaurus WordNet and ontology of any domain for retrieving information in order to capture the context of particular concept(s) and discover semantic relationships between them. reaction. Index terms – Word Sense Disambiguation, Semantic Information Retrieval, Clustering, Ontology. I. INTRODUCTION

Ontology Based Query Expansion Using Word Sense Disambiguation

An enhanced text detection technique (ETDT) is proposed, which is expected to aid the visually impaired to overcome their reading challenges. This work enhances the edge-preserving maximally stable extremal regions (eMSER) algorithm using the pyramid histogram of oriented gradients (PHOG). Histogram of oriented gradients (HOG) derived from different pyramid levels is important while detecting maximally stable extremal regions (MSER) in the ETDT approach because it gives more spatial information when compared to HOG information from a single level. To group text, a four-line, text-grouping method is newly designed for this work. Also, a new text feature, Shapeness Score is proposed, which significantly identifies text regions when combined with the other features based on morphology and stroke widths. Using the feature vector of dimension 10, the J48 decision tree and AdaBoost machine learning algorithms identify the text regions in the images. The algorithm yields better results than the existing benchmark algorithms for the ICDAR 2011 born-digital dataset and must be improved with respect to the scene text dataset.

S. Valli

Papers

A Survey on Content Based Image Retrieval

Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil

An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform ☆

Ontology Based Query Expansion Using Word Sense Disambiguation

An enhanced text detection technique for the visually impaired to read text