New Fuzzy-Mass Based Features for Video Image Type Categorization

Roy, Sambuddha; Shivakumara, Palaiahnakote; Jain, Namita; Khare, Vijeta; Pal, Umapada; Lü, Tong

doi:10.1109/icdar.2017.142

Cited by 2 publications

(1 citation statement)

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“…The combination of features is fed to the Support Vector Machine (SVM) classifier for the classification. A fuzzy logic is used in [6] to determine which edge components are straight and which are curved for each video frame. Then, the local and global mass estimates are made by drawing a series of contiguous ellipses over edge images.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Video Indexing Approach Using Decision Tree

Madheswaran¹,

Murugaiyan²

2022

IJASIS

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A systematic categorization approach for video indexing is presented in this paper. The amount of multimedia information that can be accessed over the internet continues to expand exponentially. Due to this growth and development of multimedia on the internet, particularly videos, there has been a rise in the need for video retrieval. The goal of this work is to discover subsets of characteristics that are suitable for indexing or categorizing video content. The features chosen from the frames of the video have dominant texture characteristics. Several statistical features have been applied for better performance with decision tree classification. The investigation included 1000 videos from different video contents, such as news, cartoon, advertisements, movies, and sports categories. Results show that the overall misclassification rate percentage was below 3%. The capability of indexing the video contents indicates the real power of the proposed system, which can enhance existing indexing services, thereby enriching the tools that are available for video indexing.

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Section: Introductionmentioning

confidence: 99%