Salient Segmentation based Object Detection and Recognition using Hybrid Genetic Transform

Ahmed, Abrar; Jalal, Ahmad; Rafique, Adnan Ahmad

doi:10.1109/icaem.2019.8853834

Cited by 59 publications

(16 citation statements)

References 47 publications

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“…For silhouette segmentation of depth images, we used Otsu’s thresholding method in which an image is divided into two classes—i.e., background class and foreground class [ 67 ]. In this method, multiple iterations with possible threshold values T are performed and one unique value of T is chosen that best separates foreground and background pixels.…”

Section: Proposed System Methodologymentioning

confidence: 99%

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Jalal

Khalid

Kim

2020

Entropy

Self Cite

108

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Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features—i.e., spatio-temporal features, energy-based features, shape based angular and geometric features—and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man–machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

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Section: Proposed System Methodologymentioning

confidence: 99%

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Jalal

Khalid

Kim

2020

Entropy

Self Cite

108

View full text Add to dashboard Cite

show abstract

“…Such models work on various features extracted from the sensor's data and perform human and object detection. For example, research works [42,43] focus on human interaction recognition with the help of artificial neural networks. The genetic algorithm is applied to identify prominent objects under varying environmental settings.…”

Section: Related Workmentioning

confidence: 99%

DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

Ashraf

Hur

Park

et al. 2019

Sensors

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A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

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“…By analyzing the hotspots and keywords of international research in a certain field, scholars can provide necessary reference and identify implications for the development direction, policy formulation, knowledge base, and frontier trends of the field [73][74][75][76]. In addition to scientometrics and knowledge mapping, many scholars and experts also use other algorithms and technologies to study AI, such as data extraction, features fusion, and classification and recognition technologies [77][78][79][80][81][82][83][84]. Based on these studies, from the perspective of evolution and cooperation, this study used the methods of scientometrics and knowledge map visualization to research, which is helpful for further enrichment of the field and gives this research a certain uniqueness and novelty.…”

Section: Knowledge Map Visualization Analysis Of International Researmentioning

confidence: 99%

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

2019

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In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China's international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009-2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China's AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009)(2010)(2011)(2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013)(2014)(2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016)(2017)(2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China's artificial intelligence.abstract symbols using logical rules) was the most productive way to pursue a computer that could "think"; however, as originally unimagined by Turing and others, the strategy of constructing an inference engine did not achieve the initial cognitive tasks, and it seemed that the theoretically possible concepts had not produced many feasible applications in practice [1]. Li and Wang gave five common definitions of artificial intelligences (AI) in their book Artificial Intelligence: (1) AI is a computer program which makes people feel inconceivable; (2) AI is a computer program similar to human thinking;(3) AI is a computer program similar to human behavior; (4) AI is a computer program that can learn; (5) AI is a computer ...

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Salient Segmentation based Object Detection and Recognition using Hybrid Genetic Transform

Cited by 59 publications

References 47 publications

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

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