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CONTENT BASED VIDEO RETRIEVAL USING LOW-LEVEL FEATURES

Profile image of subramanyam kunisettisubramanyam kunisetti

2023, Journal of Theoretical and Applied Information Technology

Abstract

Over the past ten years, researchers have studied the Content Based Video Retrieval System, based on numerous applications, advancements, and technologies. In video retrieval systems, there is still a requirement for the high-level semantic elements as and the processing of low level materials. As a result, it inspires and motivates a lot of academics to learn more about the content Retrieval and to make more useful and effective while creating system applications. Analysis of video for retrieval of key aspects is regarded as earlier work. In this instance, input videos from YouTube are watched for analysis. After that, foreground segmentation is carried out to find a lot of tiny subset objects since each subset must identify the foreground video class. Given for feature extraction is the split region. In this study, four distinct methods for extracting features�chromatic moment, blur, color variety, and reflection features are taken into consideration. The high dimensionality characteristics are removed from the retrieved features using Principal Component Analysis since they may affect classification accuracy. The feature vectors are taken into account while combining all of the parts. The Nave Bayes classifier is used to complete the classification process. Metrics including accuracy, precision, recall, and F-measure are used to gauge how effectively video retrieval is performed. The predicted model outperforms the current strategy when the proposed model and the in-depth learning approach are compared.

Figures (10)
Figure 1: System architecture 15 December 2023. Vol.101. No 23 © 2023 Little Lion Scientific
Figure 1: System architecture 15 December 2023. Vol.101. No 23 © 2023 Little Lion Scientific
15 December 2023. Vol.101. No 23 © 2023 Little Lion Scientific
15 December 2023. Vol.101. No 23 © 2023 Little Lion Scientific
3b. Feature extraction: Four distinct models—color, opacity, color diversity, and feature reflection were used for feature extraction. The reason behind selecting these features is that which will give more accuracy. To reduce data with various dimensions affecting classification accuracy, the mean values were taken into consideration for merging, and feature merging with principle component analysis (PCA) was carried out. A video's color is typically regarded as a significant distortion during both video recording and video playback. Because of the inadequate color pre-production qualities, this distortion can make it possible to tell the difference between false and live footage. In comparison to live video, printed video may generally have less contrast and color saturation. On-screen video, in comparison, displays greater contrast and brightness than live video. The color contrast between the false video and the live video is used in this instance to evaluate the video-based color distribution. The mean of the video, standard deviation and color distribution is evaluated by using below equations:
3b. Feature extraction: Four distinct models—color, opacity, color diversity, and feature reflection were used for feature extraction. The reason behind selecting these features is that which will give more accuracy. To reduce data with various dimensions affecting classification accuracy, the mean values were taken into consideration for merging, and feature merging with principle component analysis (PCA) was carried out. A video's color is typically regarded as a significant distortion during both video recording and video playback. Because of the inadequate color pre-production qualities, this distortion can make it possible to tell the difference between false and live footage. In comparison to live video, printed video may generally have less contrast and color saturation. On-screen video, in comparison, displays greater contrast and brightness than live video. The color contrast between the false video and the live video is used in this instance to evaluate the video-based color distribution. The mean of the video, standard deviation and color distribution is evaluated by using below equations:
Fig 2: Proposed model
Fig 2: Proposed model
15 December 2023. Vol.101. No 23 © 2023 Little Lion Scientific
15 December 2023. Vol.101. No 23 © 2023 Little Lion Scientific
Table 3: Performance measures 4. EXPERIMENTAL RESULTS: A entire pixel ‘N’ is one where the value of the ith color channel's pixel is. These statistical moments are regarded as chromatic moment features for each channel. As a result, the color feature dimension is 3*3. The feature exhibit certain unique characteristics in local and smaller regions of films.
Table 3: Performance measures 4. EXPERIMENTAL RESULTS: A entire pixel ‘N’ is one where the value of the ith color channel's pixel is. These statistical moments are regarded as chromatic moment features for each channel. As a result, the color feature dimension is 3*3. The feature exhibit certain unique characteristics in local and smaller regions of films.
There are significant inter class and intra class variances in the database video. They are real- time environments that have been captured. The lighting, background, partial occlusion, and illumination are some of the current issues with this video collection. The videos are gathered in a variety of situations. The selection of an object region from the key frames is then done using foreground segmentation. Feature fusion NB classifier is used to extract and learn the features from frames and segmentation.
There are significant inter class and intra class variances in the database video. They are real- time environments that have been captured. The lighting, background, partial occlusion, and illumination are some of the current issues with this video collection. The videos are gathered in a variety of situations. The selection of an object region from the key frames is then done using foreground segmentation. Feature fusion NB classifier is used to extract and learn the features from frames and segmentation.

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