Conference Papers

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

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Subject: search.filters.subject.Relevance feedback

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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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    A Novel Approach for Real-Time Vehicle Re-identification Using Content-Based Image Retrieval with Relevance Feedback
    (Springer, 2023) Shankaranarayan, N.; Kamath S․, S.
    Automated smart traffic surveillance systems constitute a significant part of smart city environments and have attracted significant research attention in recent years. Vehicle re-identification is a major challenge in automated traffic surveillance systems in smart city environments. Vehicle re-identification is the process of retrieving instances of the target vehicle given a gallery of numerous vehicle images. Though multiple models were proposed to perform the task of vehicle re-identification, the models struggle in terms of real-world implementation because of their complexity and computational requirements. This is mainly due to the focus on computation-heavy feature extraction processes, along with complex pre-processing and post-processing steps. To address these issues, an approach incorporating content-based image retrieval techniques with deep neural models that are computationally efficient is proposed. The approach also considers relevance feedback during the post-processing phase. Experimental results reveal that the incorporation of relevance feedback technique as a post-processing technique in vehicle re-identification helps achieve significant improvement in terms of mean average precision and Rank@k. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.