Abstract
In this paper, Content Based Video Retrieval Systems performance is analysed and compared for three different types of feature vectors. These types of features are generated using three different algorithms; Block Truncation Coding (BTC) extended for colors, Kekre’s Fast Codebook Generation (KFCG) algorithm and Gabor filters. The feature vectors are extracted from multiple frames instead of using only key frames or all frames from the videos. The performance of each type of feature is analysed by comparing the results obtained by two different techniques; Euclidean Distance and Support Vector Machine (SVM). Although a significant number of researchers have expressed dissatisfaction to use image as a query for video retrieval systems, the techniques and features used here provide enhanced and higher retrieval results while using images from the videos. Apart from higher efficiency, complexity has also been reduced as it is not required to find key frames for all the shots. The system is evaluated using a database of 1000 videos consisting of 20 different categories. Performance achieved using BTC features calculated from color components is compared with that achieved using Gabor features and with KFCG features. These performances are compared again with the performances obtained from systems using SVM and the systems without using SVM.
Highlights
In this paper, Content Based Video Retrieval Systems performance is analysed and compared for three different types of feature vectors
There is significant improvement in results using Support Vector Machine (SVM) as compared to results obtained without using SVM except for one case
2) Results for video clips using Kekre’s Fast Codebook Generation (KFCG) features Fig. 7 shows results obtained by CBVR system based on KFCG features extracted from multiple frames using SVM
Summary
Researchers have developed a number of techniques, methods and systems in the field of content based video retrieval systems They are required to effectively search, index and retrieve videos from databases but the reliable and effective systems are still awaited for huge databases [6]. Researchers still face a challenge to utilize important information such as sequence of shots, temporal and motion information [5] To compensate this problem and to get better retrieval performance, a video retrieval system [2] utilized all frames of a shot instead of only the key frames so that more visual features are extracted. Result analysis is presented in section VI; problems and challenges faced by the CBVR system are discussed in section VII and it is concluded by section VIII
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