Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization

We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning. Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to minimize intra-class variance while maximizing inter-class separability to the class label space. However, standard metric learning techniques do not incorporate the class interaction information in learning the transformation matrix, which is often considered to be a bottleneck while dealing with fine-grained visual categories. As a remedy, we propose to organize the classes in a hierarchical fashion by exploring their visual similarities and subsequently learn separate distance metric transformations for the classes present at the non-leaf nodes of the tree. We employ an iterative max-margin clustering strategy to obtain the hierarchical organization of the classes. Experiment results obtained on the large-scale NWPU-RESISC45 and the popular UC-Merced datasets demonstrate the efficacy of the proposed hierarchical metric learning based RS scene recognition strategy in comparison to the standard approaches.Comment: Undergoing revision in GRS

The comparison of clinical efficacy of formoterol and fluticasone versus salmeterol and fluticasone in patients of bronchial asthma

Background: Fixed-dose combinations of Inhaled corticosteroids (ICS) and Long acting beta agonist (LABA) are established and widely used treatment for bronchial asthma when ICSs as monotherapy are ineffective. This study attempted to compare the efficacy of salmeterol and fluticasone with formoterol (newer LABA) and fluticasone in patients of bronchial asthma.Methods: An open label, randomized, prospective, parallel and comparative study of eight-week duration was conducted on 80 patients of bronchial asthma, with the collaboration of Department of pharmacology and Department of Tuberculosis and Chest Diseases Hospital, Government medical college, Amritsar. Patients in Group A were treated with 2 actuations of Formoterol and Fluticasone (6/125µg) twice daily and group B patients were treated with 2 actuations of Salmeterol and Fluticasone (50/125µg) twice daily for 8 weeks with metered dose inhaler (MDI). Patients in group A and B were assessed on day zero, 4 weeks and 8 weeks for clinical assessment and computerized spirometry for FVC, FEV1, FEV1/FVC and PEFR.Results: In group A mean±SD of FEV1 statistically significantly increased (<0.001) after eight week of therapy (1.50±0.12) from its baseline values (1.34±0.11). Similarly, in group B mean ± SD of FEV1 statistically significantly increased (<0.001) after eight weeks (1.48±0.13) from its baseline values (1.36±0.12). There was statistically significant (<0.001) improvement in other parameters of spirometry in patients of both the groups.Conclusions: It was observed that both the combination of Fluticasone + Formoterol and Fluticasone + Salmeterol are effective in the treatment of bronchial asthma

Hierarchical metric learning for optical remote sensing scene categorization

We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning. Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to minimize intraclass variance while maximizing the interclass separability to the class label space. However, standard metric learning techniques do not incorporate the class interaction information in learning the transformation matrix, which is often considered to be a bottleneck while dealing with fine-grained visual categories. As a remedy, we propose to organize the classes in a hierarchical fashion by exploring their visual similarities and subsequently learn separate distance metric transformations for the classes present at the nonleaf nodes of the tree. We employ an iterative maximum-margin clustering strategy to obtain the hierarchical organization of the classes. Experiment results obtained on the large-scale NWPU-RESISC45 and the popular UC-Merced data sets demonstrate the efficacy of the proposed hierarchical metric learning-based RS scene recognition strategy in comparison to the standard approaches

Students' participation in collaborative research should be recognised

Letter to the editor