Conference Papers

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

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Author: search.filters.author.Kamath S․, S.Subject: search.filters.subject.Deep neural networks

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    Sketch-Based Image Retrieval Using Convolutional Neural Networks Based on Feature Adaptation and Relevance Feedback
    (Springer Science and Business Media Deutschland GmbH, 2022) Kumar, N.; Ahmed, R.; B Honnakasturi, V.; Kamath S․, S.; Mayya, V.
    Sketch-based Image Retrieval (SBIR) is an approach where natural images are retrieved according to the given input sketch query. SBIR has many applications, for example, searching for a product given the sketch pattern in digital catalogs, searching for missing people given their prominent features from a digital people photo repository etc. The main challenge involved in implementing such a system is the absence of semantic information in the sketch query. In this work, we propose a combination of image prepossessing and deep learning-based methods to tackle this issue. A binary image highlighting the edges in the natural image is obtained using Canny-Edge detection algorithm. The deep features were extracted by an ImageNet based CNN model. Cosine similarity and Euclidean distance measures are adopted to generate the rank list of candidate natural images. Relevance feedback using Rocchio’s method is used to adapt the query of sketch images and feature weights according to relevant images and non-relevant images. During the experimental evaluation, the proposed approach achieved a Mean average precision (MAP) of 71.84%, promising performance in retrieving relevant images for the input query sketch images. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Deep Neural Models for Early Diagnosis of Knee Osteoarthritis and Severity Grade Prediction
    (Springer Science and Business Media Deutschland GmbH, 2022) Shenoy, T.N.; Medayil, M.; Kamath S․, S.
    Osteoarthritis (OA) is a type of arthritis that results in malfunction and eventual loss of the cartilage of joints. It occurs when the cartilage that cushions the ends of the bones wear out. OA is the most common joint disease which frequently occurs after the age of 45 in case of males and 55 in the case of females. Manual detection of OA is a tedious and labour-intensive task and is performed by trained specialists. We propose a fully automated computer-aided diagnosis system to detect and grade osteoarthritis severity as per the Kellgren-Lawrence (KL) classification. In this paper, we experiment with various approaches for automated OA detection from X-ray images. Image-level information such as content descriptors and image transforms are identified and assigned weights using Fisher scores. KL-grade is then projected using weighted nearest neighbours, and different stages of OA severity are classified. Pre-processing, segmentation, and classification of the X-ray images are achieved using data augmentation, deep neural network, and residual neural networks. We present experimental results and discussion with respect to the best-performing models in our experiments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.