Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

A NOVEL METHOD FOR EXTRACTING TEXT FROM NATURAL SCENE IMAGES AND TTS

2019 ◽  
pp. 30-33
Author(s):  
U. R. Khamdamov ◽  
M. N. Mukhiddinov ◽  
A. O. Mukhamedaminov ◽  
O. N. Djuraev
Keyword(s):  
Natural Scene ◽  
Novel Method ◽  
Natural Scene Images

Mining discriminative patches for script identification in natural scene images

2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang
Keyword(s):  
Neural Networks ◽  
Experimental Results ◽  
The Other ◽  
Natural Scene ◽  
Fixed Size ◽  
Script Identification ◽  
Aspect Ratios ◽  
Novel Approach ◽  
Public Datasets ◽  
Natural Scene Images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

scholarly journals Application of human motion recognition technology in extreme learning machine

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098321
Author(s):  
Anzhu Miao ◽  
Feiping Liu
Keyword(s):  
Extreme Learning Machine ◽  
Research Work ◽  
Human Motion ◽  
Feature Descriptor ◽  
Background Variable ◽  
Motion Recognition ◽  
Network Training ◽  
Other Information ◽  
Human Motion Recognition ◽  
Learning Machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

Devanagari Text Detection From Natural Scene Images

2020 ◽  
Vol 10 (3) ◽  
pp. 44-59
Author(s):  
Sankirti Sandeep Shiravale ◽  
R. Jayadevan ◽  
Sanjeev S. Sannakki
Keyword(s):  
Edge Detection ◽  
Image Understanding ◽  
Text Detection ◽  
Experimental Results ◽  
Combined Approach ◽  
Natural Scene ◽  
Light Conditions ◽  
The Individual ◽  
Natural Scene Images ◽  
Better Than

Text present in a camera captured scene images is semantically rich and can be used for image understanding. Automatic detection, extraction, and recognition of text are crucial in image understanding applications. Text detection from natural scene images is a tedious task due to complex background, uneven light conditions, multi-coloured and multi-sized font. Two techniques, namely ‘edge detection' and ‘colour-based clustering', are combined in this paper to detect text in scene images. Region properties are used for elimination of falsely generated annotations. A dataset of 1250 images is created and used for experimentation. Experimental results show that the combined approach performs better than the individual approaches.

A Method to Extract Essential Information from Meteorological Facsimile Charts

2018 ◽  
Vol 33 (01) ◽  
pp. 1954001
Author(s):  
Kefeng Mao ◽  
Xi Chen ◽  
Kelan Zhu ◽  
Dong Hu ◽  
Yan Li
Keyword(s):  
Template Matching ◽  
Weather Forecasting ◽  
Contour Detection ◽  
Connected Region ◽  
Good Effect ◽  
Weather System ◽  
Essential Information ◽  
The Us ◽  
Different Types ◽  
Other Information

Using image processing technology to extract important information, such as isoline and weather system of the meteorological facsimile chart, is conducive to integration with other information, and has important practical value in navigation operations, marine weather forecasting, target recognition, and image retrieval. In meteorological facsimile charts, there are many types of medium-value lines, dense lines in some areas, superimposition and presence of multiple information, such as isolines and isoline characters, intersection of specific weather system symbols, etc. For different types of contours, numeric characters, weather system symbols and other object characteristics, the corresponding object extraction and recognition methods are proposed: Remove the latitude and longitude lines and coastline in the meteorological facsimile map by basemap matching; According to the position and shape features of the figure box, extract the meteorological fax figure box, separate and remove the different character tagging information; On the basis of identifying triangles and semicircles in weather symbols of the frontal system, the frontal symbols are extracted based on the circumscribed triangles and template matching. First the contour character on the fax image is expanded into a block connected region. Determine the position of the character information by judging the number of pixels in the connected region, and then use rotation and template matching to identify the numeric character. Using the meteorological facsimile maps of the US Meteorological Center and the Japan Meteorological Center for the main information extraction, experiments show that the method of this paper has a good effect on the complete and accurate symbol extraction of frontal weather systems, and reduces the computational complexity of contour detection, isoline extraction and numerical recognition. The methods can detect some information from weather charts properly and the error rate is very low.

An Efficient Image-Based Method for Detection of Fastener on Railway

2011 ◽  
Vol 346 ◽  
pp. 731-737 ◽  
Author(s):  
Jin Feng Yang ◽  
Man Hua Liu ◽  
Hui Zhao ◽  
Wei Tao
Keyword(s):  
Image Processing ◽  
Efficient Method ◽  
Template Matching ◽  
Detection Method ◽  
Experimental Results ◽  
Feature Descriptor ◽  
Complex Environment ◽  
Computation Efficiency ◽  
The Status ◽  
Direction Field

This paper presents an efficient method to detect the fastener based on the technologies of image processing and optical detection. As feature descriptor, the Direction Field of fastener image is computed for template matching. This fastener detection method can be used to determine the status of fastener on the corresponding track, i.e., whether the fastener is on the track or missing. Experimental results are presented to show that the proposed method is computation efficiency and is robust for fastener detection in complex environment.

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