An Optimal Codebook for Content-Based Image Retrieval in JPEG Compressed Domain

Jamil, Afshan; Majid, Muhammad; Anwar, Syed Muhammad

doi:10.1007/s13369-019-03880-0

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…However, the DCT was conducted on whole images, instead of

minimum coded (MCU) unit in JPEG. DCT coefficients were also used for image retargeting [ 26 ], image retrieval [ 27 , 28 ] and image classification [ 29 ]. Compressed bitstream was used in [ 30 ] to classify images.…”

Section: Related Workmentioning

confidence: 99%

Image Classification in JPEG Compression Domain for Malaria Infection Detection

Dong

Pan

2022

J. Imaging

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Digital images are usually stored in compressed format. However, image classification typically takes decompressed images as inputs rather than compressed images. Therefore, performing image classification directly in the compression domain will eliminate the need for decompression, thus increasing efficiency and decreasing costs. However, there has been very sparse work on image classification in the compression domain. In this paper, we studied the feasibility of classifying images in their JPEG compression domain. We analyzed the underlying mechanisms of JPEG as an example and conducted classification on data from different stages during the compression. The images we used were malaria-infected red blood cells and normal cells. The training data include multiple combinations of DCT coefficients, DC values in both decimal and binary forms, the “scan” segment in both binary and decimal form, and the variable length of the entire bitstream. The result shows that LSTM can successfully classify the image in its compressed form, with accuracies around 80%. If using only coded DC values, we can achieve accuracies higher than 90%. This indicates that images from different classes can still be well separated in their JPEG compressed format. Our simulations demonstrate that the proposed compression domain-processing method can reduce the input data, and eliminate the image decompression step, thereby achieving significant savings on memory and computation time.

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“…However, the DCT was conducted on whole images, instead of

Section: Related Workmentioning

confidence: 99%

Image Classification in JPEG Compression Domain for Malaria Infection Detection

Dong

Pan

2022

J. Imaging

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“…Shot detection is an important step for video incident detection in the video-represented methods. The typical methods are absolute inter-frame difference [34], color histogram [35], frame pixel difference [36], frame correlation coefficient [37], compressed domain difference [38], edge tracking [39], motion vector [40] and some deep learning methods, such as 3-D ConvNet [41], Two-Stream CNN [42] and CNN-LSTM [43]. We compared the above method with our method in terms of the dimension of raw data, the number of parameters representing the incident, the time complexity of the feature extraction algorithm, and the memory required by the algorithm.…”

Section: ) Performance Analysismentioning

confidence: 99%

Incident Retrieval and Recognition in Video Stream Using Wi-Fi Signal

2021

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Retrieving incidents from video stream plays an important role in many computer vision applications. However, most video surveillance system can neither recognize incidents nor support contentbased retrieval before the video stream is saved into files. As an emerging type of sensing modality, Wi-Fi signal have the potential to become a signal synchronized with the video stream to perform the incidents detection and recognition. In this work, we simultaneously collect the video stream and the Wi-Fi signal in two surveillance scenarios, and develop a LSTM-based classification model that is able to recognize the incidents in surveillance scenarios. Specifically, we first deploy a video surveillance system in two scenarios to capture the video stream and the synchronized Wi-Fi signal that is very sensitive to environmental changes. Second, an incident detection method based on the entropy change of Wi-Fi signal is proposed to find out the start and end time of the incident in the CSI sequence, thus greatly reducing the computational complexity compared with shot detection in the video stream. Third, the deep network LSTM is adopted to develop an incident recognition model that would be used to classify each size-variable CSI segments into known categories corresponding to the types of incidents. Fourth, using Wi-Fi signal to locate and recognize incidents in the video stream, we build a quick content-based video retrieval system. Last, the experimental evaluation was performed on a group of real Wi-Fi signal samples. The statistical results shows that the proposed incident detection method is feasible and effective to find out the incidents in video files with an average error of 1.5 s. And the evaluation experiment results demonstrates that the proposed multi-classification model acquires an average value of 0.972, 0.973, 0.985, 0.972 and 0.962 for recall, precision, accuracy, F-1 score and Kappa coefficient, respectively.

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High-performance intra-mode accelerator for HEVC

Tariq

2019

Vis Comput

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An Optimal Codebook for Content-Based Image Retrieval in JPEG Compressed Domain

Cited by 8 publications

References 39 publications

Image Classification in JPEG Compression Domain for Malaria Infection Detection

Image Classification in JPEG Compression Domain for Malaria Infection Detection

Incident Retrieval and Recognition in Video Stream Using Wi-Fi Signal

High-performance intra-mode accelerator for HEVC

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