Content based Video Retrieval Systems Performance based on Multiple Features and Multiple Frames using SVM

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. Keywords—CBVR; KFCG; Multiple Frames; SVM; BTC; Gabor filter I. LITERATURE REVIEW AND RELATED WORK 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]. For this reason, text based searches are still in practice for the video retrieval systems [5]. A content based retrieval system was developed for commercial use [15]. Face detection method was used for image and video searches in this system. But this method also proved to be very poorly performing [8] by the automatic systems participated in the video retrieval track [16]. A hope emerged when low level features were utilized. Comparison of low level features extracted from key frames of the query and the videos from database provide better results for video retrieval systems [6]. Other useful and much more important information from videos can bring performance of the video retrieval systems to a great level of success. 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. Another system [12] integrated color and motion features for better utilization of spatiotemporal information but a fact is still relevant that an efficient image retrieval technique results in an efficient video retrieval technique where image from the query video is used as a query [8]. The system proposed here utilizes visual features from multiple frames instead of a single frame, key frames of the shot or all of its frames. The proposed system provides the much required solutions to the problems mentioned above which are, lower efficiency when only a single image is used, high computational cost when key frames are used and unavailability of proper tools for clustering algorithm. This system provides reasonable efficiency along with low computation cost. In section II, features extraction algorithms and classification are discussed; section III discusses about similarity measure; section IV shows the methodology to calculate result parameters in the proposed CBVR system, while the proposed CBVR system is elaborated in section V. 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. II. FEATURES EXTRACTION AND CLASSIFICATION Color, texture and motion features are the most useful features for classification and retrieval of videos. Color histogram proves to be useful to represent color content while extraction of Gabor features is a popular way to represent texture features [4]. A. Extraction of BTC Features Block truncation coding (BTC) is basically a compression technique for images [14]. BTC features are calculated for small blocks formed by dividing an image instead of calculating for each pixel [17], [18]. BTC is used to obtain features from color information of pixels belonging to the small blocks. BTC features from multiple frames are employed to obtain very high precision and recall values. These features can also be used for image classification and retrieval purpose. The BTC technique can be extended to RGB (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 8, 2016 101 | P a g e www.ijacsa.thesai.org images by considering each color component (red, green and blue) as a separate plane [14]. BTC features are obtained as shown in the equations (1-5).  An inter band average image (IBAI) is formed as shown in (1) ( ) ( ) ( ) ( ) ( )  Threshold values for the three color components are calculated as shown in (2) for one of the components (red).