COMPARISON OF SUPERVISED CLASSIFICATION TECHNIQUES WITH ALOS PALSAR SENSOR FOR ROORKEE REGION OF UTTARAKHAND, INDIA

The Advanced Land Observing Satellite (ALOS) is developed by the Japanese Aerospace Exploration Agency (JAXA) which was launched in the year 2006 for the Earth observation and exploration purpose. The ALOS was carrying PRISM, AVNIR-2 and PALSAR sensors for this purpose. PALSAR is L-Band synthetic aperture radar (SAR). The PALSAR sensor is designed in a way that it can work in all weather conditions with a resolution of 10 meters. In this research work we have made an investigation on the accuracy obtained from the various supervised classification techniques. We have compared the accuracy obtained by classifying the ALOS PALSAR data of the Roorkee region of Uttarakhand, India. The training ROI’S (Region of Interest) are created manually with the assistance of ArcGIS Earth and for the testing purpose, we have used the Global positioning system (GPS) coordinates of the region. Supervised classification techniques included in this comparison are Parallelepiped classification (PC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper (SAM), Spectral information divergence (SID) and Support vector machine (SVM). Later, through the post classification confusion matrix accuracy assessment test is performed and the corresponding value of the kappa coefficient is obtained. In the result, we have concluded MDC as best in term of overall accuracy with 82.3634% and MLC with a kappa value of 0.7591. Finally, a peculiar relationship is developed in between classification accuracy and kappa coefficient

GAZE LATENT SUPPORT VECTOR MACHINE FOR IMAGE CLASSIFICATION

International audienceThis paper deals with image categorization from weak supervision, e.g. global image labels. We propose to improve the region selection performed in latent variable models such as Latent Support Vector Machine (LSVM) by leveraging human eye movement features collected from an eye-tracker device. We introduce a new model, Gaze Latent Support Vector Machine (G-LSVM), whose region selection during training is biased toward regions with a large gaze density ratio. On this purpose, the training objective is enriched with a gaze loss, from which we derive a convex upper bound, leading to a Concave-Convex Procedure (CCCP) optimization scheme. Experiments show that G-LSVM significantly outperforms LSVM in both object detection and action recognition problems on PASCAL VOC 2012. We also show that our G-LSVM is even slightly better than a model trained from bounding box annotations, while gaze labels are much cheaper to collect

Gaze Latent Support Vector Machine for Image Classification Improved by Weakly Supervised Region Selection

International audienceThis paper deals with Weakly Supervised Learning (WSL), i.e. performing image classification by leveraging local information with models trained from global image labels. We propose a new WSL method which incorporates gaze features collected by an eye-tracker to guide the region selection policy. Our approach presents two appealing advantages: gaze features are cheap to collect, e.g. one order of magnitude faster than bounding boxes, and our method only requires gaze features during training, while being gaze free at test time. For this purpose, the training objective is enriched with a gaze loss, from which we derive a concave-convex upper bound, leading to an off-the-shelf CCCP optimization scheme.  Extensive experiments are conducted to validate the effectiveness of the approach for WSL image classification on two public datasets with gaze annotation, i.e. PASCAL VOC 2012 action and POET. In addition, we publicly release a new food-related dataset, the Gaze-based UPMC Food dataset (UPMC-G20), which covers 20 food categories and 2,000 images. This dataset intends to promote the research in the food-related computer vision community