scholarly journals A deep action-oriented video image classification system for text detection and recognition

2021 ◽  
Vol 3 (11) ◽  
Author(s):  
Abhra Chaudhuri ◽  
Palaiahnakote Shivakumara ◽  
Pinaki Nath Chowdhury ◽  
Umapada Pal ◽  
Tong Lu ◽  
...  
Keyword(s):  
Recognition Performance ◽  
Text Detection ◽  
Stable Region ◽  
List Type ◽  
Classification Problems ◽  
Convolutional Network ◽  
Video Images ◽  
Text Information ◽  
Detection And Recognition

Abstract For the video images with complex actions, achieving accurate text detection and recognition results is very challenging. This paper presents a hybrid model for classification of action-oriented video images which reduces the complexity of the problem to improve text detection and recognition performance. Here, we consider the following five categories of genres, namely concert, cooking, craft, teleshopping and yoga. For classifying action-oriented video images, we explore ResNet50 for learning the general pixel-distribution level information and the VGG16 network is implemented for learning the features of Maximally Stable Extremal Regions and again another VGG16 is used for learning facial components obtained by a multitask cascaded convolutional network. The approach integrates the outputs of the three above-mentioned models using a fully connected neural network for classification of five action-oriented image classes. We demonstrated the efficacy of the proposed method by testing on our dataset and two other standard datasets, namely, Scene Text Dataset dataset which contains 10 classes of scene images with text information, and the Stanford 40 Actions dataset which contains 40 action classes without text information. Our method outperforms the related existing work and enhances the class-specific performance of text detection and recognition, significantly. Article highlights The method uses pixel, stable-region and face-component information in a noble way for solving complex classification problems. The proposed work fuses different deep learning models for successful classification of action-oriented images. Experiments on our own dataset as well as standard datasets show that the proposed model outperforms related state-of-the-art (SOTA) methods.

A New Hybrid Method for Caption and Scene Text Classification in Action Video Images

2021 ◽  
pp. 2160009
Author(s):  
Lokesh Nandanwar ◽  
Palaiahnakote Shivakumara ◽  
Umapada Pal ◽  
Tong Lu ◽  
Michael Blumenstein
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Special Property ◽  
Input Image ◽  
Vital Role ◽  
Reconstructed Image ◽  
Video Images ◽  
Scene Text ◽  
Before And After

Achieving a better recognition rate for text in action video images is challenging due to multiple types of text with unpredictable actions in the background. In this paper, we propose a new method for the classification of caption (which is edited text) and scene text (text that is a part of the video) in video images. This work considers five action classes, namely, Yoga, Concert, Teleshopping, Craft, and Recipes, where it is expected that both types of text play a vital role in understanding the video content. The proposed method introduces a new fusion criterion based on Discrete Cosine Transform (DCT) and Fourier coefficients to obtain the reconstructed images for caption and scene text. The fusion criterion involves computing the variances for coefficients of corresponding pixels of DCT and Fourier images, and the same variances are considered as the respective weights. This step results in Reconstructed image-1. Inspired by the special property of Chebyshev-Harmonic-Fourier-Moments (CHFM) that has the ability to reconstruct a redundancy-free image, we explore CHFM for obtaining the Reconstructed image-2. The reconstructed images along with the input image are passed to a Deep Convolutional Neural Network (DCNN) for classification of caption/scene text. Experimental results on five action classes and a comparative study with the existing methods demonstrate that the proposed method is effective. In addition, the recognition results of the before and after the classification obtained from different methods show that the recognition performance improves significantly after classification, compared to before classification.

Semiautomatic Ground Truth Generation for Text Detection and Recognition in Video Images

2014 ◽  
Vol 24 (8) ◽  
pp. 1277-1287 ◽  
Author(s):  
Trung Quy Phan ◽  
Palaiahnakote Shivakumara ◽  
Souvik Bhowmick ◽  
Shimiao Li ◽  
Chew Lim Tan ◽  
...  
Keyword(s):  
Ground Truth ◽  
Text Detection ◽  
Video Images ◽  
Ground Truth Generation ◽  
Detection And Recognition

A Kind of ECC - KNN Classifier's Vehicle Identification Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3724-3727
Author(s):  
Lin Tao Lü ◽  
Huan Gao ◽  
Yu Xiang Yang
Keyword(s):  
Recognition Performance ◽  
Binary Classification ◽  
Recognition Algorithm ◽  
Identification Algorithm ◽  
Classification Problems ◽  
Vehicle Recognition ◽  
Vehicle Identification ◽  
Image Characteristics ◽  
Multi Class Classification

this article presents an improved classifier vehicle identification algorithm to improve the efficiency of the existing vehicle recognition algorithm. First, using edge orientation histograms to extract image characteristics, then, Error correction coding is applied to the classification of classifier, the multi-class classification problems turned into multiple binary classification problems. A large number of experimental analysis shows that the improved vehicle identification algorithm has good recognition performance and robustness. Therefore, the algorithm which this article used has high theoretical and practical value.

scholarly journals Improvement of the end-to-end scene text recognition method for “text-to-speech” conversion

2020 ◽  
Vol 18 (06) ◽  
pp. 2050052
Author(s):  
Fazliddin Makhmudov ◽  
Mukhriddin Mukhiddinov ◽  
Akmalbek Abdusalomov ◽  
Kuldoshbay Avazov ◽  
Utkir Khamdamov ◽  
...  
Keyword(s):  
Text Detection ◽  
Text Recognition ◽  
Natural Scenes ◽  
Natural Scene ◽  
Text To Speech ◽  
Convolutional Network ◽  
Full Color ◽  
Active Research ◽  
Natural Scene Images ◽  
Detection And Recognition

Methods for text detection and recognition in images of natural scenes have become an active research topic in computer vision and have obtained encouraging achievements over several benchmarks. In this paper, we introduce a robust yet simple pipeline that produces accurate and fast text detection and recognition for the Uzbek language in natural scene images using a fully convolutional network and the Tesseract OCR engine. First, the text detection step quickly predicts text in random orientations in full-color images with a single fully convolutional neural network, discarding redundant intermediate stages. Then, the text recognition step recognizes the Uzbek language, including both the Latin and Cyrillic alphabets, using a trained Tesseract OCR engine. Finally, the recognized text can be pronounced using the Uzbek language text-to-speech synthesizer. The proposed method was tested on the ICDAR 2013, ICDAR 2015 and MSRA-TD500 datasets, and it showed an advantage in efficiently detecting and recognizing text from natural scene images for assisting the visually impaired.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

Sign in / Sign up

Export Citation Format

Share Document