Video Summarization Using Higher Order Color Moments (VSUHCM)

Jadhav, Poonam S.; Jadhav, Dipti

doi:10.1016/j.procs.2015.03.140

Cited by 21 publications

(6 citation statements)

References 5 publications

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“…In our experiment, we compare the performance analysis of our proposed method with three other video summarization techniques including VSUHCM (video summarization using higher order color moments) [13], VSUMM1 [14] and VSUMM2 [14] which have used the same video. Table I, shows the comparison of the proposed method with three other video summarization techniques in terms of Compression Ratio according to (3).…”

Section: Simulation Resultsmentioning

confidence: 99%

New Approach in Video Summarization Based on Color Feature

Sarmadi¹

2017

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Video summarization is a process to create a meaningful summary of the recorded video within a short period of time. In recent years due to increase in generating videos, we need to summarize videos. General video summarization methods are divided into static and dynamic. Static summary is done by using key frames. However, the dynamic video summarization includes a sequence of small video shots that have been put together in chronological order. In this paper, video summarization is done by using static method and color-based key frame extraction. Then by applying the super resolution algorithm on selected key frames, we obtained frames with higher visual quality. The summarization was performed on several videos. Simulation results show our method produces summaries with high visual quality and computational speed.

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Section: Simulation Resultsmentioning

confidence: 99%

New Approach in Video Summarization Based on Color Feature

Sarmadi¹

2017

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“…After obtaining the lesion regions, researchers often define the color, texture, or shape features to characterize the disease state of each sample. Gaikwad and coworkers applied K-means to segment the lesion regions in the wheat leaf images and extracted the color features, such as color histogram (Stricker, 1994), color moments (Poonam and Jadhav, 2015), and the texture features [e.g., gray-Level co-occurrence matrix [GLCM] (Gadelmawla, 2004)] to construct a support-vector machine (SVM) model for the classification of wheat diseases (Gaikwad and Musande, 2017). Ali et al (2017) applied Delta E ( E) segmentation to process the leave images of diseased potatoes and extract color and texture features based on red, green, and blue (RGB), hue, saturation, value (HSV), and local binary patterns (LBP) to implement the classification of early blight and late blight (Ismail et al, 2020).…”

Section: Semantic Feature-based Modelsmentioning

confidence: 99%

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

et al. 2022

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Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

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“…Color moments were successfully used to obtain the color features [17,19]. Most of the color distribution information was found in the low-order moments.…”

Section: Color Momentsmentioning

confidence: 99%

Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation

2022

IJIES

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Sarong is a traditional cloth typically worn during formal or religious events which conventionally woven using the traditional loom. Samarinda is one of Indonesia's regions with a sarong with a distinctive pattern. Nevertheless, the majority of indigenous people cannot distinguish the various motifs of Samarinda sarongs from those of other regions in Indonesia (non-Samarinda). Therefore, it is necessary to classify the motif of sarongs. This work proposed a pattern recognition method based on computer vision for sarongs motif classification. This method required adequate features to achieve the optimal results. Accordingly, the appropriate color and texture features were investigated to obtain the most discriminative ones. This work generated features using color moments, Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP). The most discriminatory features were selected using the Correlation Based Feature Selection (CFS) and then fed into Artificial Neural Network (ANN). A total of 1000 images were used to evaluate the method and achieved the highest performance with an accuracy value of 100%.

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Video Summarization Using Higher Order Color Moments (VSUHCM)

Cited by 21 publications

References 5 publications

New Approach in Video Summarization Based on Color Feature

New Approach in Video Summarization Based on Color Feature

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation

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