Compression based clustering technique for enhancing accuracy in web scale videos

Abstract

Detection and clustering of commercial advertisements plays an important role in multimedia indexing also in the creation of personalized user content. In existing methodologies, the mining techniques were text, image, audio retrieval based on knowledge based environment and commercial video retrieval based on rule-based algorithms, logo-based algorithms, recognition based methods. The quality video with enhanced accuracy has been detected using the automated commercial and general program for video detection technique. The clustering technique implements the clustering process for the entire video to the frames and the required main frames are depended on the total amount of frames. The key frames are extracted from the video sequences and the duplicate key frames are eliminated from the video sequences. The video compression feature has been optimized using hybrid end-to-end compression technique to extract the features and reconstruct the video frames. The main encoding algorithm performs the optimization to produce the encoded frames and it is further compressed using the additional encoding algorithm. The performance results show that the proposed technique has the improved performances in terms of MSE, PSNR, SSIM, compression ratio and the computation time which is compared with the related techniques.