scholarly journals Review: Automatic Semantic Image Annotation

2016 ◽  
Vol 15 (12) ◽  
pp. 7290-7297
Author(s):  
Shereen A. Hussein ◽  
Howida Youssry Abd El Naby ◽  
Aliaa A. A. Youssif
Keyword(s):  
Digital Images ◽  
Image Annotation ◽  
Critical Issue ◽  
Image Features ◽  
Intermediate Step ◽  
Digital Cameras ◽  
Storage Devices ◽  
Automated Image Annotation ◽  
And Storage ◽  
Semantic Image Annotation

There are many approaches for automatic annotation in digital images. Nowadays digital photography is a common technology for capturing and archiving images because of the digital cameras and storage devices reasonable price. As amount of the digital images increase, the problem of annotating a specific image becomes a critical issue. Automated image annotation is creating a model capable of assigning terms to an image in order to describe its content. There are many image annotation techniques that seek to find the correlation between words and image features such as color, shape, and texture to provide an automatically correct annotation words to images which provides an alternative to the time consuming work of manual image annotation. This paper aims to cover a review on different Models (MT, CRM, CSD-Prop, SVD-COS and CSD-SVD) for automating the process of image annotation as an intermediate step in image retrieval process using Corel 5k images.

scholarly journals Semantic image annotation using convolutional neural network and wordnet ontology

2018 ◽  
Vol 7 (2.27) ◽  
pp. 56
Author(s):  
Jaison Saji Chacko ◽  
Tulasi B
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Annotation ◽  
Digital Cameras ◽  
Semantic Class ◽  
Semantic Labeling ◽  
Textual Data ◽  
Semantic Concepts ◽  
Class Labels ◽  
Semantic Image Annotation

Images are a major source of content on the web. The increase in mobile phones and digital cameras have led to huge amount of non-textual data being generated which is mostly images. Accurate annotation is critical for efficient image search and retrieval. Semantic image annotation refers to adding meaningful meta-data to an image which can be used to infer additional knowledge from an image. It enables users to perform complex queries and retrieve accurate image results. This paper proposes an image annotation technique that uses deep learning and semantic labeling. A convolutional neural network is used to classify images and the predicted class labels are mapped to semantic concepts. The results shows that combining semantic class labeling with image classification can help in polishing the results and finding common concepts and themes.

Graphene, related two-dimensional crystals, and hybrid systems for energy conversion and storage

Science ◽  
2015 ◽  
Vol 347 (6217) ◽  
pp. 1246501 ◽  
Author(s):  
Francesco Bonaccorso ◽  
Luigi Colombo ◽  
Guihua Yu ◽  
Meryl Stoller ◽  
Valentina Tozzini ◽  
...  
Keyword(s):  
Energy Conversion ◽  
Hybrid Systems ◽  
Dye Sensitized Solar Cells ◽  
Ionic Species ◽  
Two Dimensional ◽  
Storage Devices ◽  
Energy Needs ◽  
Energy Conversion And Storage ◽  
And Storage ◽  
Sensitized Solar Cells

Graphene and related two-dimensional crystals and hybrid systems showcase several key properties that can address emerging energy needs, in particular for the ever growing market of portable and wearable energy conversion and storage devices. Graphene’s flexibility, large surface area, and chemical stability, combined with its excellent electrical and thermal conductivity, make it promising as a catalyst in fuel and dye-sensitized solar cells. Chemically functionalized graphene can also improve storage and diffusion of ionic species and electric charge in batteries and supercapacitors. Two-dimensional crystals provide optoelectronic and photocatalytic properties complementing those of graphene, enabling the realization of ultrathin-film photovoltaic devices or systems for hydrogen production. Here, we review the use of graphene and related materials for energy conversion and storage, outlining the roadmap for future applications.

Research on Water Sprays Shielding Device for Transport Vehicle

2014 ◽  
Vol 707 ◽  
pp. 317-320
Author(s):  
Jian Huang
Keyword(s):  
Power System ◽  
Storage Tank ◽  
Water Storage ◽  
Small Hole ◽  
Shielding Effect ◽  
Storage Devices ◽  
Water Storage Tank ◽  
Water Sprays ◽  
Transport Vehicle ◽  
And Storage

Water sprays shielding device for transport vehicle, by use of its own power system and storage devices, make the gases within the air tank filling the water storage tank, and jet out from the small hole of roof-shaped spray tube, by means of cyclone atomizing, to form water sprays wall with shielding effect at the top and around the transport vehicle, In order to eliminate exposure symptoms for transport vehicle in the optical, infrared and radar band, to improve the battlefield viability of transport vehicles.

The Degree Code—A New Mechanism Identifier

1993 ◽  
Vol 115 (3) ◽  
pp. 627-630 ◽  
Author(s):  
C. S. Tang ◽  
Tyng Liu
Keyword(s):  
Storage System ◽  
Graph Isomorphism ◽  
Critical Issue ◽  
Mathematical Representation ◽  
Structural Synthesis ◽  
Synthesis Of Mechanisms ◽  
Code Algorithm ◽  
Isomorphic Graphs ◽  
And Storage ◽  
Isomorphism Identification

An important step in the structural synthesis of mechanisms requires the identification of isomorphism between the graphs which represents the mechanism topology. Previously used methods for identifying graph isomorphism either yield incorrect results for some cases or their algorithms are computationally inefficient for this application. This paper describes a new isomorphism identification method which is well suited for the automated structural synthesis of mechanisms. This method uses a new and compact mathematical representation for a graph, called the Degree Code, to identify graph isomorphism. Isomorphic graphs have identical Degree Codes; nonisomorphic graphs have distinct Degree Codes. Therefore, by examining the Degree Codes of the graphs, graph isomorphism is easily and correctly identified. This Degree Code algorithm is simpler and more efficient than other methods for identifying isomorphism correctly. In addition, the Degree Code can serve as an effective nomenclature and storage system for graphs or mechanisms. Although this identification scheme was developed specifically for the structural synthesis of mechanisms, it can be applied to any area where graph isomorphism is a critical issue.

Sign in / Sign up

Export Citation Format

Share Document