Multi-Label Logo Classification using Convolutional Neural Networks

The classification of logos is a particular case within computer vision since they have their own characteristics. Logos can contain only text, iconic images or a combination of both, and they usually include figurative symbols designed by experts that vary substantially besides they may share the same semantics. This work presents a method for multi-label classification and retrieval of logo images. For this, Convolutional Neural Networks (CNN) are trained to classify logos from the European Union TradeMark (EUTM) dataset according to their colors, shapes, sectors and figurative designs. An auto-encoder is also trained to learn representations of the input images. Once trained, the neural codes from the last convolutional layers in the CNN and the central layer of the auto-encoder can be used to perform similarity search through kNN, allowing us to obtain the most similar logos based on their color, shape, sector, figurative elements, overall features, or a weighted combination of them provided by the user. To the best of our knowledge, this is the first multi-label classification method for logos, and the only one that allows retrieving a ranking of images with these criteria provided by the user.This work is supported by the Spanish Ministry HISPAMUS project with code TIN2017-86576-R, partially funded by the EU

One-shot cluster-based approach for the detection of COVID–19 from chest X–ray images

Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as of 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications

Age estimation using disconnectedness features in handwriting

© 2019 IEEE. Real-time applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues. One of such applications is human age estimation based on handwriting for the purpose of immigrant checking. In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features. To make the proposed method robust to both ruled and un-ruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components. For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multi-shape components based on distance, shape and mutual position analysis of components. Furthermore, iterative k-means clustering is proposed for the classification of different age groups. Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the state-of-the-art methods

A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force

[EN] The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper we propose a weighting based on the Newton's gravitational force, so that a mass (or relevance) has to be assigned to each instance. We evaluated this idea in the kNN context over 13 benchmark data sets used for binary and multi-class classification experiments. Results in F1 score, statistically validated, suggest that our proposal outperforms the original version of kNN and is statistically competitive with the distance weighted kNN version as well.This research was partially supported by CONACYT-Mexico (project FC-2410). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Aguilera, J.; González, LC.; Montes-Y-Gómez, M.; Rosso, P. (2019). A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force. Lecture Notes in Computer Science. 11401:305-313. https://doi.org/10.1007/978-3-030-13469-3_36S3053131140

A meta-analysis of N-acetylcysteine in contrast-induced nephrotoxicity: unsupervised clustering to resolve heterogeneity

<p>Abstract</p> <p>Background</p> <p>Meta-analyses of N-acetylcysteine (NAC) for preventing contrast-induced nephrotoxicity (CIN) have led to disparate conclusions. Here we examine and attempt to resolve the heterogeneity evident among these trials.</p> <p>Methods</p> <p>Two reviewers independently extracted and graded the data. Limiting studies to randomized, controlled trials with adequate outcome data yielded 22 reports with 2746 patients.</p> <p>Results</p> <p>Significant heterogeneity was detected among these trials (<it>I</it>²= 37%; <it>p </it>= 0.04). Meta-regression analysis failed to identify significant sources of heterogeneity. A modified L'Abbé plot that substituted groupwise changes in serum creatinine for nephrotoxicity rates, followed by model-based, unsupervised clustering resolved trials into two distinct, significantly different (<it>p </it>< 0.0001) and homogeneous populations (<it>I</it>²= 0 and <it>p </it>> 0.5, for both). Cluster 1 studies (<it>n </it>= 18; 2445 patients) showed no benefit (relative risk (RR) = 0.87; 95% confidence interval (CI) 0.68–1.12, <it>p </it>= 0.28), while cluster 2 studies (<it>n </it>= 4; 301 patients) indicated that NAC was highly beneficial (RR = 0.15; 95% CI 0.07–0.33, <it>p </it>< 0.0001). Benefit in cluster 2 was unexpectedly associated with NAC-induced decreases in creatinine from baseline (<it>p </it>= 0.07). Cluster 2 studies were relatively early, small and of lower quality compared with cluster 1 studies (<it>p </it>= 0.01 for the three factors combined). Dialysis use across all studies (five control, eight treatment; <it>p </it>= 0.42) did not suggest that NAC is beneficial.</p> <p>Conclusion</p> <p>This meta-analysis does not support the efficacy of NAC to prevent CIN.</p

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Not AvailableA food bowl of India, Punjab contributes 17% to wheat and 12% to rice production per year from the country’s 3% of the net sown area. The grains thus produced are either moved along the supply chain to meet the market demands or are held under storage for future use. The types and conditions of storage structures are the most important factors in handling and storage of food grains. In Punjab, a part of the produced grains is generally stored at farm level, in structures like Bukhari (3.5 to 18 tonnes) made of mud and bricks, earthen egg shaped Bharola (40-80 kg) and galvanized metal bins (PAU model - 0.15 to 1.5 tonne). The large amount is stored commercially by government agencies or hired storage structures that accounts for 14.6 million tonnes (Mt) in warehouses, uncovered Cover and Plinth (CAP) structures and silos. Major storage is done by agencies such as: Punjab State Warehousing Corporation (covered: 5.22 Mt and uncovered: 1.03 Mt), Food Corporation of India (covered: 4.43 Mt and uncovered: 2.41 Mt), Central Warehousing Corporation (1.26 Mt) and silos (FCI and private sector 0.25 Mt). About 97% of the commercial storage is done in bags (made of jute or polypropylene woven) under covered or uncovered conditions either inside warehouse or CAP storage as compared to 3% in bulk modernized silos. The biotic agents like beetles (Coleoptera) and moths (Lepidoptera) are the common insects attacking grains under storage deteriorating the quality and affecting the quantity of stored grains. The effective management of these insects found under the prevailing storage conditions is a major challenge in Punjab, presently relying on earlier developed storage protocols for fumigation (Aluminium phosphide at 3 tablets of 3 g each/tonne with polythene cover on grain stack, for shed fumigation at 21 tablets of 3 g each/28 m3); along with prophylactic sprays, i.e., every 15 d with Malathion 50 EC (Emulsifiable Concentrate), and every three months with Deltamethrin 2.5 WP (Wettable Powder). The structures are built in such a way that aeration is carried out by opening opposite doors of the warehouse, side wall vents, and roof turbo vents. In farm level small scale storages, plant extracts such as from neem, black pepper, turmeric, and sweet flag are being used (dosage at 10 g extract/kg of grains). Research on the effect of abiotic, biotic factors, physical methods, and stored product entomology in specific grain storage of Punjab is meagre, which limits insect management and results in significant use of chemical fumigants and insecticidesNot Availabl

Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers

Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.</jats:p