Colour Image Segmentation using Fast Fuzzy C-Means Algorithm

This paper proposes modified FCM (Fuzzy C-Means) approach to colour image segmentation using JND (Just Noticeable Difference) histogram. Histogram of the given colour image is computed using JND colour model. This samples the colour space so that just enough number of histogram bins are obtained without compromising the visual image content. The number of histogram bins are further reduced using agglomeration. This agglomerated histogram yields the estimation of number of clusters, cluster seeds and the initial fuzzy partition for FCM algorithm. This is a novell approach to estimate the input parameters for FCM algorithm. The proposed fast FCM(FFCM) algorithm works on histogram bins as data elements instead of individual pixels. This significantly reduces the time complexity of FCM algorithm. To verify the effectiveness of the proposed image segmentation approach, its performance is evaluated on Berkeley Segmentation Database(BSD). Two significant criteria namely PSNR(Peak Signal to Noise Ratio) and PRI (Probabilistic Rand Index) are used to evaluate the performance. Although results show that the proposed algorithm applied to the JND histogram bins converges much faster and also gives better results than conventional FCM algorithm, in terms of PSNR and PR

Age Invariant Face Recognition using Convolutional Neural Network

In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition.  Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier

Colour Image Segmentation using Fast Fuzzy C-Means Algorithm

This paper proposes modified FCM (Fuzzy C-Means) approach to colour image segmentation using JND (Just Noticeable Difference) histogram. Histogram of the given colour image is computed using JND colour model. This samples the colour space so that just enough number of histogram bins are obtained without compromising the visual image content. The number of histogram bins are further reduced using agglomeration. This agglomerated histogram yields the estimation of number of clusters, cluster seeds and the initial fuzzy partition for FCM algorithm. This is a novell approach to estimate the input parameters for FCM algorithm. The proposed fast FCM(FFCM) algorithm works on histogram bins as data elements instead of individual pixels. This significantly reduces the time complexity of FCM algorithm. To verify the effectiveness of the proposed image segmentation approach, its performance is evaluated on Berkeley Segmentation Database(BSD). Two significant criteria namely PSNR(Peak Signal to Noise Ratio) and PRI (Probabilistic Rand Index) are used to evaluate the performance. Although results show that the proposed algorithm applied to the JND histogram bins converges much faster and also gives better results than conventional FCM algorithm, in terms of PSNR and PR

Semantic Video Concept Detection using Novel Mixed-Hybrid-Fusion Approach for Multi-Label Data

The performance of the semantic concept detection method depends on, the selection of the low-level visual features used to represent key-frames of a shot and the selection of the feature-fusion method used. This paper proposes a set of low-level visual features of considerably smaller size and also proposes novel 'hybrid-fusion' and 'mixed-hybrid-fusion', approaches which are formulated by combining early and late-fusion strategies proposed in the literature. In the initially proposed hybrid-fusion approach, the features from the same feature group are combined using early-fusion before classifier training; and the concept probability scores from multiple classifiers are merged using late-fusion approach to get final detection scores. A feature group is defined as the features from the same feature family such as color moment. The hybrid-fusion approach is refined and the "mixed-hybrid-fusion" approach is proposed to further improve detection rate. This paper presents a novel video concept detection system for multi-label data using a proposed mixed-hybrid-fusion approach. Support Vector Machine (SVM) is used to build classifiers that produce concept probabilities for a test frame. The proposed approaches are evaluated on multi-label TRECVID2007 development dataset. Experimental results show that, the proposed mixed-hybrid-fusion approach performs better than other proposed hybrid-fusion approach and outperforms all conventional early-fusion and late-fusion approaches by large margins with respect to feature set dimensionality and Mean Average Precision (MAP) values

Semantic Video Concept Detection using Novel Mixed-Hybrid-Fusion Approach for Multi-Label Data

The performance of the semantic concept detection method depends on, the selection of the low-level visual features used to represent key-frames of a shot and the selection of the feature-fusion method used. This paper proposes a set of low-level visual features of considerably smaller size and also proposes novel ‘hybrid-fusion’ and ‘mixed-hybrid-fusion’, approaches which are formulated by combining early and late-fusion strategies proposed in the literature. In the initially proposed hybrid-fusion approach, the features from the same feature group are combined using early-fusion before classifier training; and the concept probability scores from multiple classifiers are merged using late-fusion approach to get final detection scores. A feature group is defined as the features from the same feature family such as color moment. The hybrid-fusion approach is refined and the “mixed-hybrid-fusion” approach is proposed to further improve detection rate. This paper presents a novel video concept detection system for multi-label data using a proposed mixed-hybrid-fusion approach. Support Vector Machine (SVM) is used to build classifiers that produce concept probabilities for a test frame. The proposed approaches are evaluated on multi-label TRECVID2007 development dataset. Experimental results show that, the proposed mixed-hybrid-fusion approach performs better than other proposed hybrid-fusion approach and outperforms all conventional early-fusion and late-fusion approaches by large margins with respect to feature set dimensionality and Mean Average Precision (MAP) values

Impact of rumors or misinformation on coronavirus disease (COVID-19) in social media

Introduction: The coronavirus disease 2019 (COVID-19) is a respiratory tract illness resulting from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, which has spread all over the globe, making it a major public health challenge across health systems. Simultaneously, numerous rumors, misinformation, and hoaxes appeared on several social media platforms regarding the etiology, outcomes, prevention, and cure of the disease1. The pressing issue is fake news spread more rapidly in social media than the ones from reliable sources and damages the authenticity balance of news ecosystem. Methodology: These articles contained diverse study methods (survey, content analysis, interview, literature review & others) and paradigm models (quantitative, qualitative) to identify the widespread misinformation and its impacts. Conclusion: Mainstream media platforms mostly contain fake news and rumors. The long-standing issue of misinformation regarding different socio-political issues is under constant discussion