In recent years, research in the field of keyframe extraction become more attractive due to its use in advanced applications like video surveillance. In this paper, we introduce a novel algorithm of keyframe extraction which utilizes Binary Robust Invariant Scalable Keypoint features to obtain the dissimilarity level of consecutive frames and establishes shot transition boundary, from where we extract keyframes. The frame at which dissimilarity level is high is taken as a keyframe. The proposed algorithm is tested on ten different videos of animation category. Performance of the method is assessed using the evaluation metrics- Figure of merit, Detection percentage, Accuracy and missing factor. The experimental results and analysis shows improved performance of the proposed algorithm over the other state-ofthe-art methods.
In the present article, we present an algorithm of content based video retrieval using Frame fusion and Histogram of Oriented Gradients (HOG). Representative frames of database videos are pre-processed using frame fusion to get a high resolution representative frames and HOG descriptor of this high resolution representative frames represents corresponding database video. On other side, query frame also undergo frame fusion and the HOG descriptor of high resolution query frame is used to represent query frame. To retrieve videos similar to query frame, matching is done using Euclidean distance between HOG features of query frame and database representative frames. The proposed method is tested on news category videos. The proposed method randomly picks frames from database videos, instead of selecting keyframes as query frames. Performance is assessed with the parameters precision, recall, accuracy and Jaccard index. The experimental results have shown that the performance of the proposed method is performing better than other state-of-art methods.
The rapid growth of different types of images has posed a great challenge to the scientific fraternity. As the images are increasing everyday, it is becoming a challenging task to organize the images for efficient and easy access. The field of image retrieval attempts to solve this problem through various techniques. This paper proposes a novel technique of image retrieval by combining Scale Invariant Feature Transform (SIFT) and Co-occurrence matrix. For construction of feature vector, SIFT descriptors of gray scale images are computed and normalized using z-score normalization followed by construction of Gray-Level Co-occurrence Matrix (GLCM) of normalized SIFT keypoints. The constructed feature vector is matched with those of images in database to retrieve visually similar images. The proposed method is tested on Corel-1K dataset and the performance is measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods.
Human object tracking is a challenging problem in video processing applications, and is an important step toward development of surveillance system. In this paper, we have proposed a new method for tracking of a human object in video sequence which is based on Contourlet transform. We have chosen Contourlet transform as it has high directionality and represents salient features of image such as edges, curves and contours in better way as compared with wavelet transform. The proposed method is simple and does not require any other parameter except Contourlet coefficients. Results after applying the proposed method for human object tracking is compared with other state-of-theart methods in terms of visual as well as quantitative performance measures viz. Euclidean distance and Mahalanobis distance. The proposed method is found to be better than other methods.
Presence of shadow degrades performance of any computer vision system as a number of shadow points are always misclassified as object points. Various algorithms for shadow detection and removal exist for still images but very few algorithms have been developed for moving objects. This paper introduces a new method for shadow detection and removal from moving object which is based on Dual tree complex wavelet transform. We have chosen Dual tree complex wavelet transform as it is shift invariant and have a better edge detection property as compared to real valued wavelet transform. In the present work, shadow detection and removal has been done by thresholding wavelet coefficients of Dual tree complex wavelet transform of difference of reference frame and the current frame. Standard deviation of wavelet coefficients is used as an optimal threshold. Results after visual and quantitative performance metrics computation shows that the proposed method for shadow detection and removal is better than other state-of-theart methods.
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