Bio: Gautam Pal is an academic researcher from National Institute of Technology Agartala. The author has contributed to research in topics: Motion compensation & Centroid. The author has an hindex of 3, co-authored 3 publications receiving 71 citations.
••01 Jan 2015
TL;DR: This paper presents different approaches to shot boundary detection problem, and shows how segmentation plays an important role in digital media processing, pattern recognition, and computer vision.
Abstract: Video image processing is a technique to handle the video data in an effective and efficient way. It is one of the most popular aspects in the video and image based technologies such as surveillance. Shot change boundary detection is also one of the major research areas in video signal processing. Previous works have developed various algorithms in this domain. In this paper, a brief literature survey is presented that establishes an overview of the works that has been done previously. In this paper we have discussed few algorithms that were proposed previously which also includes histogram based, DCT based and motion vector based algorithms as well as their advantages and their limitations.
TL;DR: A new algorithm is proposed to detect the shot boundary by using the minimum ratio similarity measurement between the characteristic features of two consecutive frames to suggest that the precision performance of the algorithm is independent of the nature of the video.
Abstract: Video segmentation plays an essential role in digital video processing, pattern recognition, security, video conferencing, etc. The convenience of the video is based on its content which is still impossible. One major challenging task of automatic video indexing is automatic detection of video shots. In this paper, a new algorithm is proposed to detect the shot boundary by using the minimum ratio similarity measurement between the characteristic features of two consecutive frames. Where, diverse parameters are calculated for each frame that creates a feature vector of size 40. The system performance is measured in terms of metric parameters. Also, a comparative study with alternative algorithms such as rapid cut detection, histogram-based method, etc. is done. Results suggest that the precision performance of the algorithm is independent of the nature of the video. The F-measure performance comparison shows that the proposed algorithm is the best with maximum average value and minimum standard deviation.
01 Nov 2014
TL;DR: Block based exhaustive search is used for calculation of motion vector and result shows, this approach speeds up the process by 40% and having the same quality of recovered frame compared with thequality of recovered frames in serial processing.
Abstract: Video compression is the most active research area in multimedia and signal processing. The most important component in multimedia is video. From surveillance to disease diagnosis video has numerous applications. Videos generally contain huge temporal redundancy. Hence, such video requires the use of motion vector estimation technique in order to remove the temporal redundancy. Although motion vector estimation is computationally expensive. Different algorithms has been developed to reduce the computational complexity of motion vector calculation. All of these algorithms were serial. In this paper, motion vector is calculated by processing multiple frames parallely. Block based exhaustive search is used for calculation of motion vector. Computation result shows, this approach speeds up the process by 40% and having the same quality of recovered frame compared with the quality of recovered frames in serial processing.
TL;DR: In this paper, a novel chaotic bat algorithm (CBA) was proposed for multi-level thresholding in grayscale images using Otsu's between-class variance function.
Abstract: Multi-level thresholding is a helpful tool for several image segmentation applications Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA) Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321) Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives Therefore, it can be applied in complex image processing such as automatic target recognition
TL;DR: New models and algorithms for object-level video advertising that aims to embed content-relevant ads within a video stream is investigated and a heuristic algorithm is developed to solve the proposed optimization problem.
Abstract: In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.
TL;DR: The key technique for improving the algorithm efficiency, analyses the correlative algorithms and compares their properties are revealed, and the future trend of motion estimation research is discussed.
Abstract: Based on the study of Fast Block Matching Algorithms (FBMA) for motion estimation, this paper attempts to make an overview of fast block matching algorithms First, it describes the theory of motion estimation Then it reveals the key technique for improving the algorithm efficiency, analyses the correlative algorithms and compares their properties At the end, the future trend of motion estimation research is discussed
TL;DR: A novel real time integrated method to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score.
Abstract: Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.
TL;DR: This paper presents a review of an extensive set for SBD approaches and their development, and the advantages and disadvantages of each approach are comprehensively explored.
Abstract: The recent increase in the number of videos available in cyberspace is due to the availability of multimedia devices, highly developed communication technologies, and low-cost storage devices. These videos are simply stored in databases through text annotation. Content-based video browsing and retrieval are inefficient due to the method used to store videos in databases. Video databases are large in size and contain voluminous information, and these characteristics emphasize the need for automated video structure analyses. Shot boundary detection (SBD) is considered a substantial process of video browsing and retrieval. SBD aims to detect transition and their boundaries between consecutive shots; hence, shots with rich information are used in the content-based video indexing and retrieval. This paper presents a review of an extensive set for SBD approaches and their development. The advantages and disadvantages of each approach are comprehensively explored. The developed algorithms are discussed, and challenges and recommendations are presented.