Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Anantharaman, A.

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    Kinect Based Suspicious Posture Recognition for Real-Time Home Security Applications
    (Institute of Electrical and Electronics Engineers Inc., 2018) Vikram, M.; Anantharaman, A.; Suhas, B.S.; Ashwin, T.S.; Guddeti, R.M.R.
    Suspicious posture recognition is a paramount task with numerous applications in everyday life. We explore one such application in real-time home security using the Microsoft Kinect depth camera. We propose a novel method where the remote device itself detects the suspicious activity. The server is alerted by the remote device in case of a suspicious activity which further alerts the home owners immediately. We show that our method, works in real-time, is robust towards changing lighting conditions and the computations happen on the remote device itself which makes it suitable for real-time home security. © 2018 IEEE.
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    An approach for multimodal medical image retrieval using latent dirichlet allocation
    (Association for Computing Machinery, 2019) Vikram, M.; Suhas, B.S.; Anantharaman, A.; Kamath S․, S.S.
    Modern medical practices are increasingly dependent on Medical Imaging for clinical analysis and diagnoses of patient illnesses. A significant challenge when dealing with the extensively available medical data is that it often consists of heterogeneous modalities. Existing works in the field of Content based medical image retrieval (CBMIR) have several limitations as they focus mainly on visual or textual features for retrieval. Given the unique manifold of medical data, we seek to leverage both the visual and textual modalities to improve the image retrieval. We propose a Latent Dirichlet Allocation (LDA) based technique for encoding the visual features and show that these features effectively model the medical images. We explore early fusion and late fusion techniques to combine these visual features with the textual features. The proposed late fusion technique achieved a higher mAP than the state-of-the-art on the ImageCLEF 2009 dataset, underscoring its suitability for effective multimodal medical image retrieval. © 2019 Association for Computing Machinery.
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    Performance evaluation of topic modeling algorithms for text classification
    (Institute of Electrical and Electronics Engineers Inc., 2019) Anantharaman, A.; Jadiya, A.; Sai Siri Chandana, C.T.S.; Adikar Bharath, N.V.S.; Mohan, B.R.
    Text Classification is a paramount task in natural language processing. Topic modeling algorithms have been used with a lot of success for text classification. We evaluate different topic modeling algorithms for two tasks: (1) Short text or sentence classification and (2) Large text or document classification. We give an extensive performance evaluation with the help of a wide range of performance metrics for three topic modeling algorithms on both of these tasks using three publicly available datasets. ©2019 IEEE.
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    A multi-space approach to zero-shot object detection
    (Institute of Electrical and Electronics Engineers Inc., 2020) Gupta, D.; Anantharaman, A.; Mamgain, N.; Kamath S․, S.; Balasubramanian, V.N.; Jawahar, C.V.
    Object detection has been at the forefront for higher level vision tasks such as scene understanding and contextual reasoning. Therefore, solving object detection for a large number of visual categories is paramount. Zero-Shot Object Detection (ZSD) - where training data is not available for some of the target classes - provides semantic scalability to object detection and reduces dependence on large amount of annotations, thus enabling a large number of applications in real-life scenarios. In this paper, we propose a novel multi-space approach to solve ZSD where we combine predictions obtained in two different search spaces. We learn the projection of visual features of proposals to the semantic embedding space and class labels in the semantic embedding space to visual space. We predict similarity scores in the individual spaces and combine them. We present promising results on two datasets, PASCAL VOC and MS COCO. We further discuss the problem of hubness and show that our approach alleviates hubness with a performance superior to previously proposed methods. © 2020 IEEE.