Cricket Video Highlight Generation Methods: A Review

In this paper, a novel method for the detection of events in cricket videos using Umpire hand signals or gestures is proposed. Important events in the game are signaled by Umpire with unique gestures. Scene segmentation of cricket video is initially carried out by the detection of bowling events. Then the Umpire frames are identified from each scene and analyzed using both vertical and horizontal intensity projection profiles. These profiles representing the Umpire gestures in the frame, can be used as features for training an ensemble classifier Random Forest (RF) for the extraction of events-FOUR, SIX, NO BALL, OUT and WIDE in cricket game. The method is tested with recorded video of some international Twenty -Twenty cricket matches and found that event identification using Umpire gestures matches with manually detected events.

Watching sports videos over streaming sites and television network is one of the most entertaining ways to engage with sports activities. Sports videos like cricket has been viewed by larger audiences than viewing in person. The pandemic since 2020 has changed the world of sports viewing to a larger extent. Some of the sports events are even streamed live through YouTube. The most interesting part of any sports videos is watching the highlights or events of great interest. This is because of the lack of time to watch the entire length of the game. Automatic video summarization is the solution to this. Some of the day long sports like cricket needs the summarization to be very precise and bring the content within few minutes to the audience. There are several attempts in the literature to automatically summarize the sports videos, particularly the game of cricket. In this paper, an attempt has been made to review some of the latest developments in creating the video summary of cricket sports. A brief review of existing methods of video summarization that addresses many sports including soccer, cricket, tennis, and basketball are reviewed at the beginning. Later, the methods that are developed based on latest machine learning and high-performance algorithms are discussed in detail. Towards the end of this paper, a comparison of these methods is presented. The goal is to lead the prospective researchers in the direction where the methods have open avenues and scope to strengthen.

Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma.

Cricket is the most popular sport after football. Indian Premier League or the IPL is the most popular T20 domestic league in the world. Cricket involves lots of data and statistics. In the game of cricket, several parameters can be used to predict the outcome of the game. The factors affecting a cricket match can be combined with Machine Learning to predict the outcome of a match. This research has focused on analysing the features of the cricket matches in IPL. Moving further towards the analysis of the Indian Premier League, this paper has rated the Batsmen and Bowlers in a unique way based on their performance. A few crucial factors like team form and team strength in predicting the match outcome apart from the conventional features like the toss, venue of the games etc., have been added. Further, a novel analysis of Batting and Bowling has been proposed based on Batting Index and Bowling Index. Machine Learning algorithms like SVM, Logistic Regression, Random Tree, Random Forest and Naive Bayes have been applied, for match predictions. Lastly, the results, based on which algorithm gives the best accuracy, have been plotted. Decision Tree and Logistic Regression algorithms have given an accuracy over 87% and 95% respectively.

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

We aimed to evaluate the benefit of adding CT texture analysis on conventional CT features of benign adnexal cystic lesions, especially in identifying mucinous cystadenoma. This retrospective study included patients who underwent surgical removal of benign ovarian cysts (44 mucinous cystadenomas, 32 serous cystadenomas, 16 follicular/simple cysts and 43 endometriotic cysts) at our institution between January 2015 and November 2017. The CT images were independently reviewed by an abdominal radiologist (reviewer 1) and a resident (reviewer 2). Both reviewers recorded the conventional characteristics and performed texture analysis. Based on reviewer 1's results, two decision trees for differential diagnosis were developed. Reviewer 2's results were then applied to the decision trees. The diagnostic performances of each reviewer with and without the decision trees were compared. Several conventional features and texture analysis parameters showed significant differences between mucinous cystadenomas and other benign adnexal cysts. The first decision tree selected septum number and thickness as significant features, whereas the second decision tree selected septum number and the mean values at spatial scaling factor (SSF) 0. Reviewer 1's performance did not change significantly with or without the use of the decision trees. Reviewer 2's interpretations were significantly less sensitive than reviewer 1's interpretations (p = 0.001). However, when aided by the first and second decision trees, Reviewer 2's interpretations were significantly more sensitive than reviewer 1's interpretations (86.4%, p < 0.001; 72.7%, p = 0.001). This study suggests the benefit of CT texture analysis on conventional images to differentiate mucinous cystadenoma from other benign adnexal cysts, particularly for less experienced radiologists.

Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to the IMV algorithm to generate ten voted results. The most representative was chosen using a greedy technique to calculate the overall classification performance of the model. The HHF-MUPTT-based ECG beat classification model attained excellent performance, with the best lead-wise accuracy of 99.85% observed in Lead III and 99.94% classification accuracy using the IMV algorithm. The results confirmed the high MI classification ability of the presented computationally lightweight HHF-MUPTT-based model.

Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similarity within ground-penetrating radar (GPR) profiles. We have observed that the intricate and often visually indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar subsurface characteristics. In this study, we therefore examined the feasibility of using textural features extracted from GPR data to automate subsurface characterization. The textural features were matched to a fingerprint database of previous subsurface classifications of GPR textural features and the corresponding physical probings of subsurface conditions. Four textural features (energy, contrast, entropy, and homogeneity) were selected as inputs into a neural-network classifier. This classifier was tested and verified using GPR data obtained from two distinctly different field sites. The first data set contained features that indicate the presence or lack of sandstone bedrock in the upper 2 m of a shallow soil profile of fine sandy loam and loam. The second data set contained columnar patterns that correspond to the presence or the lack of vertical preferential flow paths within a deep loessial soil. The classifier automatically grouped each data set into one of the two categories. Comparing the results of classification using extracted textural features to the results obtained by visual interpretation found 93.6% of the sections that lack sandstone bedrock correctly classified in the first set of data, and 90% of the sections that contain pronounced columnar patterns correctly classified in the second set of data. The classified profile sections were mapped using integrated GPR and GPS data to show ground surface boundaries of different subsurface conditions. These results indicate that textural features extracted from GPR data can be utilized as inputs in a neural network classifier to rapidly characterize and map the subsurface into categories associated with known conditions with acceptable levels of accuracy. This approach of GPR imagery classification is to be considered as an alternative method to traditional human interpretation only in the classification of voluminous data sets, wherein the extensive time requirement would make the traditional human interpretation impractical.

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.

Image analysis-based classification of pigmentation patterns in fish: A case study of pseudo-albinism in Senegalese sole

This paper discusses an event detection approach based on audio, which proves to be effective when applied to the audio component of cricket television broadcaster’s video. In this approach, both crowed noise levels which is in the background and commentator voice which is in focus are considered correlated to key events in the time frame. Classifiers related to Hidden Markov Model (or HMM) are utilized as it is an efficient tool for modeling processes varying with time and broadly used in the area of speech recognition. This experiment done using cricket television broadcaster videos, was successful thus this method can be used for automatic indexing of audio (or video containing audio) for quick searching or segmentation.

Global economic development and increasing human activities have brought great challenges to fragile ecosystems. In order to avoid, reduce, and reverse desertification, Chinese and foreign scientists and ecological governance institutions have developed a series of ecological restoration technologies (ERTs) and models in the past few decades. These technologies can improve residents' livelihoods, strengthen disaster resilience, and launch a comprehensive review of degraded ecosystems in desertification regions. However, some studies and practices have limited the selection and promotion of good technologies and the assessments of these technologies, resulting in the waste and loss of funds and manpower. The objective of this study is to identify desertification control and restoration technologies and models, summarize the evolutionary features and trends of these technologies under different natural conditions, and evaluate the various ERTs that are now available. The data sources of this study include the databases of international organizations, CNKI, related literature and reports, and questionnaires from institutions and experts. First, the three stages of ERTs evolution were summarized, and the key events and social-economic developments were identified as the driving forces of evolution. Then, the four categories of ERTs were identified as biological, engineering, agricultural, and management ERTs. Finally, the key ERTs were evaluated in the five dimensions of the degree of difficulty, the degree of maturity, effectiveness, suitability, and potential for transfer. The management ERTs scores for the degree of difficulty, the degree of maturity, and potential for transfer are higher. This study provides a reference for adapting to local conditions, the comprehensive management, rational development, and utilization of dryland resources, improving the application of ecological technologies, and promoting the export and import of the excellent technologies.

Using a perennial model plant allows the study of reoccurring seasonal events in a way that is not possible using a fast-growing annual such as A. thaliana (Arabidopsis). In this study, we present a hybrid aspen (Populus tremula×P. tremuloides) as our perennial model plant. These plants can be grown in growth chambers to shorten growth periods and manipulate day length and temperature in ways that would be impossible under natural conditions. In addition, the use of growth chambers allows easy monitoring of height and diameter expansion, accelerating the collection of data from new strategies that allow evaluation of promoters or inhibitors of growth. Here, we describe how to study and quantify responses to seasonal changes (mainly using P. tremula×P. tremuloides) by measuring growth rate and key events under different photoperiodic cycles.

This research was conducted on microstructural images of four different metals such as mild steel, stainless steel, aluminium, and copper. The research aims to classify the microstructure images of the four metals obtained using light-optical microscope using texture features and machine learning techniques. The images were subjected to different steps like image pre-processing, texture feature extraction and classification. Classical machine learning algorithms like Support Vector Machines, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and XGBoost were used. Experimental results show that the XGBoost classifier achieves the highest classification accuracy of 98% using normalized texture features and cross validation using 10-folds.

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.