In computer vision, identification of actions of an object is considered as a complex and relevant task. When solving the problem, one requires information on the position of key points of the object. Training models that determine the position of key points requires a large amount of data, including information on the position of these key points. Due to the lack of data for training, the paper provides a method for obtaining additional data for training, as well as an algorithm that allows highly accurate recognition of animal actions based on a small number of data. The achieved accuracy of determining the key points positions within a test sample is 92%. Positions of the key points define the action of the object. Various approaches to classifying actions by key points are compared. The accuracy of identifying the action of the object in the image reaches 72.9 %.

Near real-time animal action recognition and classification

Use of electronic gadgets is increasing in this digital era as people often tend to use digital devices to perform there day to day activities. More and more fields such as government offices, banking sectors, educational institutions and so on use digital information for ease of storage and access. Today, the world is moving towards the paperless offices than using the physical paper documents. Paper documents once scanned get converted into document images. These document images have complex layouts which make it difficult to process. Processing these document images is a document image analysis problem known as DIA problem. It's been observed that the document image dataset is exponentially increasing which has simple to very complex structure leading to complex problems in processing. In order to find suitable solutions to these issues we intend to make use of a deep learning based approach for layout equivalence detection which can help to solve the retrieval problems and which may also be useful in the area of digital forensics based on the document layouts. Proposed approach follows two stages in which the first stage performs object detection using deep learning model followed by creation of bounding boxes for different entities which is further used to compare the layouts of the document images to predict the matching layouts for equivalence. Proposed model is a combination of object detection, bounding box creation and extraction of layouts which further uses Brute force matcher to perform equivalence detection. Here, we also present the state of the art document image datasets that is widely used by the researchers in the literature for solving DIA problems. Some of the publicly available datasets are considered for analyzing the results. The results show the robustness of the proposed approach.

Deep learning based Layout Equivalence Detection in Compressed Domain and analysis of different Document Image Dataset: A Systematic Study

Image Understanding provides a description of a scene without the help of a user. To make this ability feasible, machine learning (ML) performs an imperative role. Learning algorithm permits the system to understand an image reliant on its training algorithm. The notable areas of mining algorithms are contents-based image retrieval, change detection, video sequence analysis, video retrieval, image knowledge extraction, object recognition and model learning. The combination of deep learning and mining algorithms are a great boon to image understanding. This paper handles various methods involved in image understanding. As image understanding is a popular area of present research, it is significant to know how far the researchers contributed to this field until today. This paper analyzes various techniques and models for image understanding.

Review based on Image Understanding Approaches

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Review based on Image Understanding Approaches