Rough-fuzzy based scene categorization for text detection and recognition in video

Roy, Sambuddha; Shivakumara, Palaiahnakote; Jain, Namita; Khare, Vijeta; Dutta, Anjan; Pal, Umapada; Lü, Tong

doi:10.1016/j.patcog.2018.02.014

Cited by 27 publications

(14 citation statements)

References 44 publications

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“…The method is developed specifically for Marathon images, which depicts a very specific scene setting. The methods [8] explores the combination of rough set and fuzzy for classifying scene images based on text and background information. The method extracts feature for each classified edge component on the classification of images.…”

Section: Related Workmentioning

confidence: 99%

“…The same criterion is followed for all the classification experiments in this work. To test the scalability and effectiveness of the proposed method on classification, we consider the dataset used for video image type categorization in [8],…”

Section: Dataset Performance Measure and Work For Comparisonmentioning

confidence: 99%

“…This issue motivated us for considering text detection and recognition to enhance the performance of action image detection and recognition. The advantage of text detection and recognition is that the process does not require temporal information unlike existing methods that explore temporal frames for action detection [8].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Dataset Performance Measure and Work For Comparisonmentioning

confidence: 99%

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

et al. 2021

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“…This method may not work well for still images as it requires temporal frames for achieving accurate results. Later, Roy et al [13] proposed roughfuzzy based scene categorization for text detection and recognition in video. The method defines shapes based on edge components for the classification of videos of different text types.…”

Section: Related Workmentioning

confidence: 99%

A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images

Nandanwar

Shivakumara

Manna³

et al. 2020

Pattern Recognition and Artificial Intelligence

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Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification.

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“…The constituent elements of different scenes are quite different, so distinct effects may be obtained when the same recognition method is used to handle different scene datasets, especially for outdoor and indoor scenes. Current research in scene classification faces many challenges (Roy et al, 2018). Firstly, scenes are complex and diverse, and scene images may have a large difference even captured in the same type of scene.…”

Section: Introductionmentioning

confidence: 99%

A scene recognition algorithm based on deep residual network

Mao

Wang

et al. 2019

Systems Science & Control Engineering

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Scene recognition is quite important in the field of robotics and computer vision. Aiming at providing high performance and universality of feature extraction, a convolutional neural network-based scene recognition model entitled Scene-RecNet is proposed. To reduce parameter space and improve the feature quality, deep residual network is introduced as the feature extractor. A feature adjustment layer composed of a convolutional layer and a fully connected layer is added after the feature extractor to further synthesize and compress the extracted features. Migration learning-based 'pre-training and fine-tuning' mode is used to train Scene-RecNet. The feature extractor is pre-trained by Ima-geNet, and the overall network performance is fine-tuned on specific data sets. Experiments show that comparing with other algorithms, the features obtained by Scene-RecNet have high generality and robustness, and Scene-RecNet can provide better scene classification accuracy rate.

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Rough-fuzzy based scene categorization for text detection and recognition in video

Cited by 27 publications

References 44 publications

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

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

A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images

A scene recognition algorithm based on deep residual network

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