Eagle Eye CBVR Based on Unique Key Frame Extraction and Deep Belief Neural Network

Abstract

Great efforts have been paid to develop Content Based Video Retrieval (CBVR) due to the exponential growth of video datasets with several types of data such as visual, audio and metadata. This paper proposes Eagle Eye a new Content Based Video Retrieval Framework through applying new methods for the major three important parts of CBVR. A Subspace Clustering Algorithm is implemented with binary codes that are extracted from different videos. We introduce a Local Histogram based shot boundary detection algorithm to detect shot boundaries. Then Unique Key Frame Summarization algorithm with detected shots is applied. An integrated Local Binary Pattern, Coherence based filter and HOG algorithm is used for the Feature extraction and best features are selected and stored along with the video indices. The extracted features were then utilized to train a classifier. A Deep Belief Neural Network classifier trains each extracted feature of query frame and matches the best features of the query frames with other video features. Then we enhance the video retrieval by introducing video hashing method, combining low level and high-level semantic features for the elimination of repetitive video in the retrieval. We used precision, accuracy, sensitivity and specificity metrics to assess the applicability of the projected technique. Xiph.org and Youtube datasets are used for the valuation analysis. The experimental results show that the Eagle Eye provides better performance and less processing time compared to the other methods.