Human Action Recognition (HAR) involves human activity monitoring task in different areas of medical, education, entertainment, visual surveillance, video retrieval, as well as abnormal activity identification, to name a few. Due to an increase in the usage of cameras, automated systems are in demand for the classification of such activities using computationally intelligent techniques such as Machine Learning (ML) and Deep Learning (DL). In this survey, we have discussed various ML and DL techniques for HAR for the years 2011–2019. The paper discusses the characteristics of public datasets used for HAR. It also presents a survey of various action recognition techniques along with the HAR applications namely, content-based video summarization, human–computer interaction, education, healthcare, video surveillance, abnormal activity detection, sports, and entertainment. The advantages and disadvantages of action representation, dimensionality reduction, and action analysis methods are also provided. The paper discusses challenges and future directions for HAR.

A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications

A review of computer vision technologies for plant phenotyping

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.

/pdf/knn-based-machine-learning-approach-for-text-and-document-1nud2wa0ve.pdf

KNN based Machine Learning Approach for Text and Document Mining

In Agriculture, leaf diseases have grown to be a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automated recognition of diseases on leaves plays a crucial role in agriculture sector. This paper imparts a simple and computationally proficient method used for leaf disease identification and grading using digital image processing and machine vision technology. The proposed system is divided into two phases, in first phase the plant is recognized on the basis of the features of leaf, it includes pre-processing of leaf images, and feature extraction followed by Artificial Neural Network based training and classification for recognition of leaf. In second phase the disease present in the leaf is classified, this process includes K-Means based segmentation of defected area, feature extraction of defected portion and the ANN based classification of disease. Then the disease grading is done on the basis of the amount of disease present in the leaf.

Leaf disease detection and grading using computer vision technology & fuzzy logic

Machine learning approach for text and document mining

Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising.

/pdf/infected-fruit-part-detection-using-k-means-clustering-3jcymm3jw7.pdf

Infected Fruit Part Detection using K-Means Clustering Segmentation Technique

Vision-based human activity recognition is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here the authors propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. The authors contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that their method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.

Human Activity Recognition Using Gait Pattern

The volume of digital images generated and uploaded on the internet every day by the scientific, medical, educational, industrial and other communities are very large. The problem of retrieving the desired images from huge collections is a major problem. The user queries are becoming very specific and traditional text-based methods cannot efficiently handle them. The subjectivity of human perception and the rich contents of the images further aggravate the problem. To overcome this problem, a new query-by-example technique using multiple color, texture and shape features is proposed and evaluated in this paper. The experimental results suggest that our proposed technique is efficient and retrieves semantically more similar images.

Semantic Image Retrieval by Combining Color, Texture and Shape Features

Human activity recognition based on the computer vision is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here we propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. We contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that our method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.

/pdf/analysis-of-gait-pattern-to-recognize-the-human-activities-4p5xt3ujsg.pdf

Analysis of Gait Pattern to Recognize the Human Activities

In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar images, the need for databases, the semantic gap, and retrieving the desired images from huge collections are the keys to improve. CBIR system analyzes the image content for indexing, management, extraction and retrieval via low-level features such as color, texture and shape. To achieve higher semantic performance, recent system seeks to combine the low-level features of images with high-level features that conation perceptual information for human beings. Performance improvements of indexing and retrieval play an important role for providing advanced CBIR services. To overcome these above problems, a new query-by-image technique using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.

/pdf/semantic-image-retrieval-using-multiple-features-1rwbfwobqz.pdf

Semantic image retrieval using multiple features

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