Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags

This paper presents a retargeting approach based on semantic analysis. Our approach has better performance in images that have comlicated backgroud or multi-objects. Our aim is to protect important object in retargeting process. So we brought in object recognition and image importance calculation for retargeting. This approach consists of three parts: object recognition, the importance of image calculation and image retargeting. (i) Firstly we optimized semantic texton forest (STF)[11]and got much better results of object recognition.(ii) Secondly we presented a method called dynamically adjust importance image calculation. (iii) Thirdly we give a retargeting method based on triangular mesh. According to importance image we cover Delaunay triangular mesh on image and solve optimization mesh transforming based on energy function which subjects to least energy loss and boundary restraint. Compared with previous approaches, our method has better result in some complex scene images.

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Quantifying the difficulty of object recognition tasks via scaling of accuracy vs. training set size

Frontiers in Neuroscience ◽

10.3389/conf.fnins.2010.03.00325 ◽

2010 ◽

Vol 4 ◽

Author(s):

Kenyon Garrett

Keyword(s):

Object Recognition ◽

Training Set ◽

Set Size

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Ontology-Based Framework for Cooperative Learning of 3D Object Recognition

Applied Sciences ◽

10.3390/app11178080 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8080

Author(s):

Parkpoom Chaisiriprasert ◽

Karn Yongsiriwit ◽

Matthew N. Dailey ◽

Chutiporn Anutariya

Keyword(s):

Knowledge Management ◽

Object Recognition ◽

Cooperative Learning ◽

Web Application ◽

Semantic Analysis ◽

Service Robots ◽

3D Object Recognition ◽

Cloud Infrastructure ◽

3D Object ◽

Robotic Applications

Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots.

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Military Object Detection in Defence using Multi-Level Capsule Networks

10.21203/rs.3.rs-330732/v1 ◽

2021 ◽

Author(s):

B Janakiramiaha ◽

Kalyani G ◽

Karuna A ◽

Narasimha Prasad L V ◽

Krishna M

Keyword(s):

Neural Network ◽

Object Recognition ◽

Target Detection ◽

Network Architecture ◽

Training Data ◽

Support Vector ◽

Training Set ◽

Automatic Target Detection ◽

Security Issues ◽

Multi Level

Abstract Automatic target detection plays a major role in automated war operations. The key concept behind automated target detection is military objects recognition from the captured images. For object recognition in the given image, Convolutional Neural Network (CNN) is a powerful classification network. But in general CNNs are trained for general object recognition. But, the performance of CNN depends mainly on the size of the training set. The size of the training data is generally available in less proportion for military objects due to its operational and security issues. Hence the performance of CNN may degrade sharply. To address the issue of military objects, a relatively new neural network architecture called Capsule Network (CapsNet) is introduced. Hence, in this article, a variant of CapsNet called Multi-level CapsNet framework is projected for military object recognition under the case of small training set. The introduced framework of this paper is validated on a dataset of military objects which are collected from the internet. The dataset contains particularly five military objects and the similar civil ones. The proposed framework demonstrates a large improvement of 96.54% of accuracy for military object recognition. Experiments demonstrate that the proposed framework can accomplish a high recognition precision, superior to many other algorithms such as conventional Support Vector Machines and transfer learning based CNNs.

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