Summary
In recent years, massive growth in the number of images on the web has raised the requirement of developing an effective indexing model to search digital images from a large‐scale database. Though cloud service offers effective indexing of compressed images, it remains a major issue due to the semantic gap between the user query and diverse semantics of large‐scale database. This article presents a radix trie indexing (RTI) model based on semantic visual indexing for retrieving the images from cloud platforms. Initially, an interactive optimization model is applied to identify the joint semantic and visual descriptor space. Next, an RTI model is applied to integrate the semantic visual joint space model for finding an effective solution for searching large‐scale sized dataset. Finally, a Spark distributed model is applied for deploying the online image retrieval service. The performance of the proposed method is validated on two standard dataset, namely, Holidays 1 M and Oxford 5 K in terms of mean average precision (mAP) and processing time under varying dataset sizes. During experimentation, the presented RTI model shows the maximum mAP value of 0.83 under the dataset size of 1000. Similarly, under the sample count of 1000, it is noted that the standalone server requires a maximum of 118 minutes to complete the process, whereas the spark cluster requires a minimum of around only 19 minutes to finish the process. The experimental outcome showed improvement in terms of various measures over the best rivals in the literature.
Recently, industrial Internet of things becomes more popular and it involves a group of intelligent devices linked to create systems which observe, gather, communicate, and investigate data. In this view, the demand for compression techniques in remote sensing images is increasing since low complexity technique is required in spacecraft. Deep learning, for instance, convolutional neural network (CNN) has gained more attention in the domain of computer vision, particularly for high‐level applications like detection along with interpretation. At the same time, it is difficult to resolve the low‐level applications like image compression and it is investigated in this article. This article presents an optimal compression technique using CNNs for remote sensing images. The proposed method uses CNN for learning the compact representation of the original image which held the structural data and was then coded by Lempel Ziv Markov chain algorithm. Next, the encoded image was reconstructed to retrieve the original image with high reconstructed image quality. The proposed optimal compression technique is compatible with the available image codec standards. Wide range of experiments was carried out and the results were compared with binary tree and optimized truncation, JPEG, and JPEG2000 in terms of compression efficiency, reconstructed image quality, and space saving (SS). The obtained results apparently proved the effectiveness of the presented method, which attains an average peak signal to noise ratio of 49.90 dB and SS of 89.38%.
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