Abstract
Image retrieval is the most significant technology forever. Computer vision is improving its trends and methodologies to perform like a human. System can identify any object without a human help by a simple query. To train the system, we need precise algorithms. Content-based image retrieval (CBIR) is the recent emerging trend in computer vision to retrieve relevant images from huge amount of data. This paper used a distributed and parallel processing paradigm to accelerate the retrieval process. SPARK stream processing environment is used on the top of the Hadoop Distributed File System (HDFS). To extract visual content of the images, edge and texture features are used. The distance between query image and database is measured with Mahalanobis distance metric. The performance of the retrieval system has been compared with other distributed image retrieval systems. The proposed methodology using SPARK environment outperforms the existing systems.
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Sudheer, D., SethuMadhavi, R., Balakrishnan, P. (2019). Edge and Texture Feature Extraction Using Canny and Haralick Textures on SPARK Cluster. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_56
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DOI: https://doi.org/10.1007/978-981-13-1610-4_56
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