Sparse Coded Handcrafted and Deep Features for Colon Capsule Video Summarization

Mohammed, Ahmed; Yayilgan, Sule Yildirim; Pedersen, Marius; Hovde, Øistein; Cheikh, Faouzi Alaya

doi:10.1109/cbms.2017.13

Cited by 8 publications

(6 citation statements)

References 35 publications

(37 reference statements)

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“…The comparison metrics have given a better score with other methods with reasonable performance. This method can be easily used for (quasi) real-time VS and anomaly detection, also extendable with other advanced sparse-land approaches as CSC (Convolutional Sparse Coding Model) [24], and K-SVD approaches [55,56]. Finding an optimal threshold function or value for summarization is still open as the performance measure gets affected as we decrease or increase the threshold function.…”

Section: A Evaluation Of Video Summarymentioning

confidence: 99%

Artificial Bee Colony Algorithm Optimization for Video Summarization on VSUMM Dataset

Paramanantham¹,

Sureshkumar²

2020

IJACSA

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This paper attempts to prove that the Artificial Bee Colony algorithm can be used as an optimization algorithm in sparse-land setup to solve Video Summarization. The critical challenge in doing quasi(real-time) video summarization is still time-consuming with ANN-based methods, as these methods require training time. By doing video summarization in a quasi (real-time), we can solve other challenges like anomaly detection and Online Video Highlighting. A simple threshold function is tested to see the reconstruction error of the current frame given the previous 50 frames from the dictionary. The frames with higher threshold errors form the video summarization. In this work, we have used Image histogram, HOG, HOOF, and Canny edge features as features to the ABC algorithm. We have used Matlab 2014a for doing the feature extraction and ABC algorithm for VS. The results are compared to the existing methods. The evaluation scores are calculated on the VSUMM dataset for all the 50 videos against the two user summaries. This research answers how the ABC algorithm can be used in a sparse-land setup to solve video summarization. Further studies are required to understand the performance evaluation scores as we change the threshold function.

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Section: A Evaluation Of Video Summarymentioning

confidence: 99%

Artificial Bee Colony Algorithm Optimization for Video Summarization on VSUMM Dataset

Paramanantham¹,

Sureshkumar²

2020

IJACSA

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“…This curve is generated by combining normalized scores. Mohammed et al in [13] used multiple features for colon video summarization. Methodology involves grouping of video frames based on power spectral density.…”

Section: Removal Of Redundant Framesmentioning

confidence: 99%

Video summarization approaches in wireless capsule endoscopy: A review

Raut

Gunjan

2020

E3S Web Conf.

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Wireless capsule endoscopy (WCE) is medical examination process for gastrointestinal tract (GIT). This noninvasive multi advantageous procedure can be made more popular by overcoming the problem of prolonged analysis time. Video summarization is a concise and meaningful representation of a video. Along with automated detection and segmentation methods, summarized video will serve as an additional source for confirming the analysis of WCE video before the final diagnosis without any dropouts. Nowadays, Internet of Things (IoT) environments are predominant in healthcare sectors. Considering limited resources of smart phones and long duration of WCE process, it is impractical to send all the WCE data to health-care centers or gastroenterologists. This paper reviews video summarization types and the techniques used for WCE video summarization by various researchers. Feature set selection, clustering methods and key frame selection techniques play important role in performance of summarization technique.

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“…Frame interpolation can be used to enhance CCE for better visualization by increasing the The MSE value in italics indicate the best performing method frame rate and improving capsule battery life. In literature, there are recent works that aim to detect informative segments automatically [20,21]. Increasing the frame rate of these segments will assist the gastroenterologist to go through the video quickly.…”

Section: Applicability Of Interpolation For Cce Video Framesmentioning

confidence: 99%

Variational approach for capsule video frame interpolation

Mohammed¹,

Farup²,

Yayilgan³

et al. 2018

J Image Video Proc.

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Capsule video endoscopy, which uses a wireless camera to visualize the digestive tract, is emerging as an alternative to traditional colonoscopy. Colonoscopy is considered as the gold standard for visualizing the colon and takes 30 frames per second. Capsule images, on the other hand, are taken with low frame rate (average five frames per second), which makes it difficult to find pathology and results in eye fatigue for viewing. In this paper, we propose a variational algorithm to smooth the video temporally and create a visually pleasant video. The main objective of the paper is to increase the frame rate to be closer to that of the colonoscopy. We propose variational energy that takes into consideration both motion estimation and intermediate frame intensity interpolation using the surrounding frames. The proposed formulation incorporates both pixel intensity and texture feature in the optical flow objective function such that the interpolation at the intermediate frame is directly modeled. The main feature of this formulation is that error in motion estimation is incorporated in our model, so that only robust motion estimation are used in estimating the intensity of the intermediate frame. We derived Euler-Lagrange equations and showed an efficient numerical scheme that can be implemented on graphics hardware. Finally, a motion compensated frame rate doubling version of our method is implemented. We evaluate the quality of both 90 and 100% of the frames for medical diagnosis domain through objective image quality metrics. Our method improves state-of-the-art result for 90% frames while performing equivalent for the remaining cases with other existing methods. In the last section, we show application of frame interpolation to informative frame segment visualization and to reduce the power consumption.

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Sparse Coded Handcrafted and Deep Features for Colon Capsule Video Summarization

Cited by 8 publications

References 35 publications

Artificial Bee Colony Algorithm Optimization for Video Summarization on VSUMM Dataset

Artificial Bee Colony Algorithm Optimization for Video Summarization on VSUMM Dataset

Video summarization approaches in wireless capsule endoscopy: A review

Variational approach for capsule video frame interpolation

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