Volume-07 , Special Issue-05 , Mar - 2019 Go Back

Content-Based Image Retrieval (CBIR), also known as Query by Image Content and Content-Based Visual Information Retrieval is the application of Computer Vision Techniques and Image Processing Algorithms to the image retrieval problem which is the problem of searching for digital images in large databases. "Content-based" means that the search analyses the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "Content" refers the low – level features of the image such as colour, shape, texture, or any other information that can be derived from the image itself. Content based image retrieval uses these extracted features to retrieve the relevant images from the database. The Local and Global features extracted from these image also plays an important role in the Content Based Medical Image Retrieval (CBMIR). The global features are extracted from the whole image whereas the zone based local features are computed from individual regions of the image to form the local features. Recent studies show that content based image retrieval is an important area of research in the multimedia databases in retrieving similar images based on user defined specification or pattern. In this paper we analyse the different state-of-art local and global feature extraction techniques used by the content based image retrieval system for medical images.

CBIR,CBMIR,FeatureExtraction,GlobalandLocalFeature,Color,Texture,Shape,ImageRetreival

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[6] Neha Ghosh, Shikha Agrawal, Mahesh Motwani ,”A Survey of Feature Extraction for Content-Based Image Retrieval System”(2018).
[7] V. Murugesh, V. Sivakumar, P. Janarthanan ,”An Ingenious Segmentation Application for Brain Lesion Detection in Multimodal MR Images”, ISSN: 2347-2693(2018).
[8] Zahid Mehmood, Fakhar Abbas, Toqeer Mahmood, Muhammad Arshad Javid, AmjadRehman, Tabassam Nawaz1, “Content-Based Image Retrieval Based on Visual Words Fusion Versus Features Fusion of Local and Global Features” (2017) .
[9] MahyaSadeghi, Parmit K. Chilana, and M. Stella Atkins ,”How Users Perceive Content-Based Image Retrieval for Identifying Skin Images”, ISSN: 141–148(2018).
[10] Khawaja Tehseen Ahmed, AunIrtaza, MuhammadAmjad Iqbal,” Fusion of local and global features for effective image extraction” ,ISSN : 526–543(2017).
[11] AbdolraheemKhaderAlhassan, Ali Ahmed Alfaki ,”Color and Texture Fusion-Based Method for Content-Based Image Retrieval”(2017) .[12] Rafael S. Bressan, Daniel H. A. Alves, Lucas M. Valerio, Pedro H. Bugatti , Priscila T. M. Saito, “DOCToR: The Role of Deep Features in Content-based Mammographic Image Retrieval”, ISSN: 2372-9198(2018).
[13] BehzadMerhrbakhshChoobari, Saeed Mozaffari, ”A Robust Content Based Image Retrieval Using Local Full-Directional Pattern (LFDP)”, (2017).
[14] N. Parvin, P. Kavitha ,”Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm” ,pp226-233(2017).
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[20] Vikram M Kakade, Ishwar A. Keche ,“Review on Content Based Image Retrieval (CBIR) Technique”, ISSN: 2319-7242(2017).
[21] JC Kavitha, Suruliandi, Nagarajan ,”Melanoma Detection in Dermoscopic Images using Global and Local Feature Extraction”, ISSN: 1975-0080(2017).

Jasmine Samraj, R.Dhivya, "A Survey on Local and Global Feature Extraction Techniques in Content Based Medical Image Retrieval", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.251-265, 2019.

In the recent ten to fifteen years web developers and web users expend more amount of time on images and videos. Since video is an admirable tool for delivering content, it has one of the major roles in human daily life. There are many kinds of videos available in real life and therefore we need an important tool to perform classification on video-based applications. Video classification and video content analysis is one of the ongoing research areas in the field of computer vision. The main goal of video classification is to help the viewers to find video of their own interest. We need a tool to classify the video with sky scramble accuracy. Therefore, we propose a model for video classification with several medium layers. This model takes video as an input passed through various layers and produce the video class label. The class label may be sports, movies, advertisement, cartoon, news etc.

Video Classification, Keyframe, Video Frame, Background Subtraction

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M. Ramesh, K. Mahesh, "A Preliminary Investigation on a Novel Approach for Efficient and Effective Video Classification Model", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.266-269, 2019.

Consider a single server queueing system with Loss and Feedback in which customers arrive in a Poisson process with arrival rate λ and service time follows an exponential distribution with parameter μ. If the server is free at the time of an arrival of a customer, the arriving customer begins to be served immediately by the server and satisfied customer leaves the system with probability (1-q) after the service completion and dissatisfied customers will join the queue with probability q to get service once again. This is called Feedback in queueing terminology. If the server is busy, then the arriving customer will join the queue with probability p in front of service station. This is called Loss in queueing terminology. In this paper, we have derived the closed form solutions of time dependent probabilities of the single server queueing systems with Loss and Feedback. The corresponding Transient distributions have been obtained. We also obtain the time dependent performance measures of the systems.

Loss and Feedback - Single Server - Steady State Probabilities –System performance measures- Transient Probability Distributions

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S. Shanthi Sivanandam, A. Muthu Ganapathi Subramanian, Gopal Sekar, "Transient Analysis of Single Server Queueing system with Loss and Feedback", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.270-274, 2019.

Nowadays child security is an important area of concern. This model is developed to rectify the worries of parents regarding their child security. This paper proposed a model for child safety through smart phones that provides the option to track the location of their children as well as in case of emergency notification is send via E-mail.Mobile phones can be used to enhance student’s services. One of these services is taking the attendance. Taking attendance requires a location factor of the student. Hence, iBeacon can be used for this purpose. iBeacon is not only used for marking attendance it is also used for location a child who roaming inside the school campus. The exact location and the time how long he/she spend in that location is transferred to the class teacher.Automated learning analytics is becoming an important topic in the educational community, which needs effective systems to monitor learning process and provide feedback to the teacher and parent. Student affective states such as happy, sad, fear, disgust, surprise, angry, neutral are automatically determined from facial expressions.

GPS, iBeacon, Mood Prediction

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[14] Minh-Son Dao1, Duc-Tien Dang-Nguyen, Asem Kasem1 and Hung Tran-The, “Healthy Classroom- A Proof-of-Concept Study for Discovering Students’ Daily Moods and Classroom Emotions to Enhance a Learning-teaching Process using Heterogeneous Sensors”, In the 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2018), pp.685-691, 2018.
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C.Mary Shiba, R. Dhanalakshmi, "SMART CHILD SECURITY SYSTEM BASED ON IoT", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.275-282, 2019.

Artificial Intelligence is one of the most prominent fields in computer sciences in the twenty first century. Using the combination of machine learning and neural networking various autonomous assistants can be built serving a wide range of purposes. The integration of AI with other fields would mean a ground breaking feat in the field of technology, perhaps comparable to the innovation of mobile phones and their integration with internet. The IoT AI integration is crucial for further development in technology as it improves connectivity greatly between devices. A rover is built using this union of technologies. The rover is equipped with a processor to make calculations and handle the AI component. With the advancements in strong AI the capacity of performance that can be achieved by the Rover becomes limitless. The other integration with a possible future is the one with quantum technologies, with an increased amount of rovers there can be quantum server farms(for speed) set up in geographically suitable regions which would increase the speed and connectivity of the internet and more importantly the security as it is ensured by the use of lava lamp or compound pendulum encryption.

Artificial Intelligence, Machine Learning, Neural Networking, Quantum computing, Rover, Server Farms, Encryption

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S. Naveen Joe, "A Discussion on Ways to Integrate Artificial Intelligence with Pinnacles of Technology in the Contemporary World", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.283-287, 2019.