Ontology based multiobject segmentation and classification in sports videos

Abstract

The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes.

Mixed artificial reasoning, closer to human?

“…Feature selection aims at reducing the number of variables to keep only the most relevant without changing the initial variables (Gomathi and Karlekar, 2019;Mendez et al, 2019). On the contrary, Feature extraction change the initial variables using the prior knowledge of the ontology for get relevant features (Kumar et al, 2020;Radovanovic et al, 2019;Evert et al, 2019;Agarwal et al, 2015;Radinsky et al, 2012;Greenbaum et al, 2019;Liu et al, 2021;Rinaldi et al, 2021;Castillo et al, 2008;Yilmaz, 2017;Hsieh et al, 2013;Rajput and Haider, 2011;Manuja and Garg, 2015;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Pérez-Pérez et al, 2021;Zhao et al, 2021;. In semantic embedding, always in training data step, raw data are both refined by semantic knowledge and transformed into vectors to be exploited by neural networks (Chen et al, 2021;Ren et al, 2020;Qiu et al, 2019;Ali et al, 2019;Zhang et al, 2019;Makni and Hendler, 2019;Benarab et al, 2019;Moussallem et al, 2019;Gaur et al, 2019;Jang et al, 2018;Hassanzadeh et al, 2020;Ali et al, 2021;Amador-Domínguez et al, 2021;Alexandridis et al, 2021;Niu et al, 2022), SVM…”

“…Table 8 presents machine learning algorithms of each informed machine learning category. Articles were mainly published after 2017, and a great part of them concern neural networks (Hassanzadeh et al, 2020;Gaur et al, 2019;Ali et al, 2019;Jang et al, 2018;Zhang et al, 2019;Ali et al, 2021;Amador-Domínguez et al, 2021;Benarab et al, 2019;Chen et al, 2021;Wang et al, 2021bWang et al, , 2010Sabra et al, 2020;Pancerz and Lewicki, 2014;Yilmaz, 2017;Kumar et al, 2020;Rinaldi et al, 2021;Gomathi and Karlekar, 2019;Serafini et al, 2017;Kuang et al, 2021;Chung et al, 2020;Fu et al, 2015;Huang et al, 2019;Abdollahi et al, 2021;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Zhao et al, 2021), especially Recurrent Neural Networks (Makni and Hendler, 2019;Ren et al, 2020;Moussallem et al, 2019;Zhang et al, 2019;Jang et al, 2018;Ali et al, 2021;Liu et al, 2021;Huang et al, 2019;Alexandridis et al, 2021;Niu...…”

“…(2021);Deepak et al (2022);Akila et al (2021), ANN:Yilmaz (2017), CNN:Kumar et al (2020);Messaoudi et al (2021), RNN: Nayak et al (2021); Zhou et al (2021)(LSTM), Liu et al (2021)(GRU), Word2Vec: Kumar et al (2020) VGG16: Rinaldi et al (2021) SVM Agarwal et al (2015); Greenbaum et al (2019); Manuja and Garg (2015) Probabilistic system Bayesian network: Rajput and Haider (2011), Naïve Bayes: Evert et al (2019), Decision tree: Castillo et al (2008)(C4.5), EM: Castillo et al (2008) Linear model Regression logistic: Radovanovic et al (2019) Multiple algorithms Hsieh et al (2013); Ahani et al (2021) Clustering Castillo et al (2008), k-means: Pérez-Pérez et al (2021), HAC: Radinsky et al ((2014), ANN: Wang et al (2021b, 2010), MLP: Sabra et al (2020), RNN, CNN et HAN: Abdollahi et al (2021) Probabilistic system HMM: Pozveh et al (2018), Bayesian network: Santos et al (2012), Decision tree: Akand et al ((2019); Chen et al (2021), MLP: Hassanzadeh et al (2020), CNN: Gaur et al (2019), RNN:Makni and Hendler (2019); Ren et al (2020), Niu et al (2022)(GRU), Moussallem et al (2019); Zhang et al (2019); Jang et al (2018); Ali et al (2021); Alexandridis et al (2021)(LSTM), Autoencoders: Benarab et al (2019), Word2Vec: Jang et al (2018); Zhang et al (2019); Ali et al (2021); Amador-Domínguez et al (2021) SVM Mabrouk et al (2020) Continued on next pageTable 8 -Continued from previous page Informed ); Chung et al (2020), LSTM: Huang et al (2019), CNN:Kuang et al (2021), CAE: Fu et al (2015), Serafini et al (2017)(LTN) learning algorithm Probabilistic system Bayesian networks: Palazzo et al (2021), Messaoud et al (2015)(CBN), Markov: Ruiz-Sarmiento et al (2019) (CRF), Decision tree: Emele et al (2012)(C4.5) Ensemble methods Random Forest: Rizzo et al (2017)…”

Salient object detection: a mini review

“…Identifying salient areas in an image can facilitate subsequent advanced visual tasks, enhancing efficiency and resource management and improving performance (Gupta et al, 2020). Thus, SOD can help filter irrelevant backgrounds, and SOD plays a significant pre-processing role in computer vision applications, providing important basic processing for these applications, e.g., segmentation (Donoser et al, 2009;Qin et al, 2014;Noh et al, 2015;Fu et al, 2017;Shelhamer et al, 2017), classification (Borji and Itti, 2011;Joseph et al, 2019;Akila et al, 2021;Liu et al, 2021;Jia et al, 2022;Ma and Yang, 2023), tracking (Frintrop and Kessel, 2009;Su et al, 2014;Ma et al, 2017;Lee and Kim, 2018;Chen et al, 2019), etc.…”