Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

/pdf/a-comprehensive-survey-on-particle-swarm-optimization-1d380v5xx1.pdf

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

A comprehensive survey for scheduling techniques in cloud computing

In the multimedia era a large volume of video data can be recorded during a certain period of time by multiple cameras. Such a rapid growth of video data requires both effective and efficient multiview video summarization techniques. The users can quickly browse and comprehend a large amount of audiovisual data. It is very difficult in real-time to manage and access the huge amount of video-content-handling issues of interview dependencies significant variations in illumination and presence of many unimportant frames with low activity. In this paper we propose a local-alignment-based FASTA approach to summarize the events in multiview videos as a solution of the aforementioned problems. A deep learning framework is used to extract the features to resolve the problem of variations in illumination and to remove fine texture details and detect the objects in a frame. Interview dependencies among multiple views of video are then captured via the FASTA algorithm through local alignment. Finally object tracking is applied to extract the frames with low activity. Subjective as well as objective evaluations clearly indicate the effectiveness of the proposed approach. Experiments show that the proposed summarization method successfully reduces the video content while keeping momentous information in the form of events. A computing analysis of the system also shows that it meets the requirement of real-time applications.

F-DES: Fast and Deep Event Summarization

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. It is a herculean task to manage access to video content in real time where humongous amount of audiovisual recorded data is generated every second. In this paper we propose an Eratosthenes Sieve based key-frame extraction approach for video summarization (VS) which can work better for real-time applications. Here, Eratosthenes Sieve is used to generate sets of all Prime number frames and nonprime number frames up to total N frames of a video. k-means clustering procedure is employed on these sets to extract the key–frames quickly. Here, the challenge is to find the optimal set of clusters, achieved by employing Davies-Bouldin Index (DBI). DBI a cluster validation technique which allows users with free parameter based VS approach to choose the desired number of key-frames without incurring additional computational costs. Moreover, our proposed approach includes likes of both local and global perspective videos. The method strongly enhances clustering procedure performance trough engagement of Eratosthenes Sieve. Qualitative and quantitative evaluation and complexity computation are done in order to compare the performances of the proposed model and state-of-the-art models. Experimental results on two benchmark datasets with various types of videos exhibit that the proposed methods outperform the state-of-the-art models on F-measure.

Eratosthenes sieve based key-frame extraction technique for event summarization in videos

A novel approach to combine features for salient object detection using constrained particle swarm optimization

Due to advancement in digital multimedia technologies, huge amount of video contents in such arenas as sports, surveillance, news, etc., is becoming widely available. Due to factors like presence of inactive frames, inter-view dependencies and frequent illumination changes, whole video content may not be important. Moreover, users may not have the adequate time to access/ store the entire video in real time. We propose a machine learning ensemble method to summarize the events in multiview videos, as a solution to the aforementioned issues. In order to make the accurate decision on keyframes, we trained our ensembles using meta approach where the inter-dependency and illumination changes of views are considered in the training phase. Experimental results on two benchmark datasets show that our model outperforms the state-of-the-art models with the best Recall and F-measure. The computing analysis of the model also indicates that it meets all requirements of real-time applications.

Event BAGGING: A novel event summarization approach in multiview surveillance videos

The rapid growth of video data demands both effective and efficient video summarization methods so that users are allowed to speedily browse and comprehend a large amount of video content. Hence, it is very challenging to store and access such audiovisual information in real time where an immense amount of recorded video content is rising within one second. In this paper we proposed an equal partition based clustering technique for summarizing the events in videos which can work better for real time applications (for e.g., surveillance video in various security systems). In clustering, the difficulty is to obtain the optimal set of clusters, which is gained by implementing Davies-Bouldin Index, a cluster validation technique which permits the users with free parameter based video summarization method for selecting the numbers of key–frames without incurring additional computational cost. The qualitative as well as quantitative evaluation is done in order to compare the performances of our proposed model and state-of-theart models. Experimental results on two benchmark datasets with various types of videos expose that the proposed method outperforms the state-of-the-art models with the best Precision and F–measure.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNALlcjK/pdf

Navjot Singh

Papers

Eratosthenes sieve based key-frame extraction technique for event summarization in videos

A novel approach to combine features for salient object detection using constrained particle swarm optimization

Event BAGGING: A novel event summarization approach in multiview surveillance videos

Equal Partition Based Clustering Approach for Event Summarization in Videos

A convex hull approach in conjunction with Gaussian mixture model for salient object detection