Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction

Content Based Video Retrieval (CBVR) is so popular these days, because of the increased utilization of video based analytical systems. Video based analytics is quite effective than image analysis, as a series of actions are captured by the video. This ends up with better decision making ability. The CBVR systems play an important role in boosting the human–computer interaction. This paper presents a multimodal CBVR that takes both the visual and audio information into account for retrieving relevant videos to the user. Two modules are employed by this work to deal with video and audio data. The video data is processed to detect the significant frame from shots and is achieved by Lion Optimization Algorithm (LOA). The features are extracted from the visual data and with respect to the audio data, MHEC and LPCC features are extracted. The extracted features are clustered by Kernelized Fuzzy C Mean (KFCM) algorithm. Finally, the feature database is formed and is utilized in the query matching process during the testing phase. The performance of the proposed work is tested in terms of precision, recall, F-measure and time consumption rates. The proposed CBVR system proves better performance than the existing approaches and is evident through attained results.