Content based Video Retrieval Systems Performance based on Multiple Features and Multiple Frames using SVM
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 8
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.
Authors and Affiliations
Mohd Ansari, Hemlata Vasishtha
Keywords
Related Articles
- EP ID EP149362
- DOI 10.14569/IJACSA.2016.070815
- Views 110
- Downloads 0
Survey of Nearest Neighbor Condensing Techniques
The nearest neighbor rule identifies the category of an unknown element according to its known nearest neighbors’ categories. This technique is efficient in many fields as event recognition, text categorization and objec...
A Novel Image Encryption Supported by Compression Using Multilevel Wavelet Transform
In this paper we propose a novel approach for image encryption supported by lossy compression using multilevel wavelet transform. We first decompose the input image using multilevel 2-D wavelet transform, and thresholdin...
TX-Kw: An Effective Temporal XML Keyword Search
Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. Existing methods cannot resolve challenges addressed by using keyword search in Tem...
Analysis of Coauthorship Network in Political Science using Centrality Measures
In recent era, networks of data are growing massively and forming a shape of complex structure. Data scientists try to analyze different complex networks and utilize these networks to understand the complex structure of...
Competitive Sparse Representation Classification for Face Recognition
A method, named competitive sparse representation classification (CSRC), is proposed for face recognition in this paper. CSRC introduces a lowest competitive deletion mechanism which removes the lowest competitive sample...