Results 1 -
7 of
7
Active Learning: Any Value for Classification of Remotely Sensed Data?
, 2013
"... Member, IEEE Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
(Show Context)
Member, IEEE Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the “most informative ” and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We provide an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines. We discuss remote sensing specific approaches dealing with multi-source and spatially and time-varying data, and provide examples for high dimensional hyperspectral and very high resolution multispectral imagery.
An adaptable image retrieval system with relevance feedback using kernel machines and selective sampling
- IEEE Trans. Image Process
, 2009
"... Abstract—This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, mul ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Abstract—This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, multiple mapping subsystems that adaptively modify the similarity measures, and a relevance feedback mechanism that incorporates user information. The adaptation process drives the retrieval error to zero in order to exactly meet either an existing multiclass classification model or the user high-level concepts using reference-model or relevance feedback learning, respectively. To facilitate the selection of the most informative query images during relevance feedback learning a new method based upon the Fisher information is introduced. Model-reference and relevance feedback learning mechanisms are thoroughly tested on a domain-specific image database that encompasses a wide range of underwater objects captured using an electro-optical sensor. Benchmarking results with two other relevance feedback learning methods are also provided. Index Terms—Content-based image retrieval, Fisher information matrix and selective sampling, in-situ underwater target identification, kernel machines, regularization, relevance feedback learning. I.
Object Classification in Sidescan Sonar Images with Sparse Representation Techniques
- Proceedings of the International Conference on Acoustics, Speech and Signal Processing
, 2012
"... Most supervised classification approaches try to learn patterns in in-ter class variabilities using training samples. However in the real world, their discriminative power is often diminished, because data is seldom free from irregularities within a class. Apriori modeling of these intra class varia ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Most supervised classification approaches try to learn patterns in in-ter class variabilities using training samples. However in the real world, their discriminative power is often diminished, because data is seldom free from irregularities within a class. Apriori modeling of these intra class variabilities poses a challenge even in underwa-ter sidescan sonar images that we consider for object classification in this work. Sparse representation techniques prove particularly useful in this regard because of their data driven approach to model these variabilities. Results on the NSWC sidescan sonar database suggest that sparse representation classifier with zernike magnitude features is significantly robust in the presence of these non-idealities.
unknown title
, 2007
"... Detection of anomalies in texture images using multi-resolution random field models ..."
Abstract
- Add to MetaCart
Detection of anomalies in texture images using multi-resolution random field models
Group Testing
"... A strategy for active target detection suitable for the use of mobile agents in a field is presented. In particular, there is an interest in autonomous underwater vehicles. By exploiting notions from group testing, the proposed algorithm decides when to collect new samples depending on whether the m ..."
Abstract
- Add to MetaCart
(Show Context)
A strategy for active target detection suitable for the use of mobile agents in a field is presented. In particular, there is an interest in autonomous underwater vehicles. By exploiting notions from group testing, the proposed algorithm decides when to collect new samples depending on whether the mobile agent perceives the sensor mea-surements correspond to noise or a target pattern. Under suitable assumptions about the field emanated by the target, i.e. the target signature is locally low rank in the field, one can efficiently sample the field to locate the target using O(m logm logn) samples on an n × n grid where m n is a parameter specifying the group size.
Automated Identification of Hard Exudates and Cotton Wool Spots using Biomedical image Processing
"... The automatic identification of Image processing techniques for abnormalities in retinal images. Its very importance in diabetic retinopathy screening. Manual annotations of retinal images are rare and exclusive to obtain. The ophthalmoscope used direct analysis is a small and portable apparatus con ..."
Abstract
- Add to MetaCart
(Show Context)
The automatic identification of Image processing techniques for abnormalities in retinal images. Its very importance in diabetic retinopathy screening. Manual annotations of retinal images are rare and exclusive to obtain. The ophthalmoscope used direct analysis is a small and portable apparatus contained of a light source and a set of lenses view the retina. The existence of diabetic retinopathy detected can be examining the retina for its individual features. The first presence of diabetic retinopathy is the form of Microaneurysms. This research paper describes different works needed to the automatic identification of hard exudates and cotton wool spots in retinal images for diabetic retinopathy detection and support vector machine (SVM) for classifying images. This system is evaluated on a large dataset containing 129 retinal images. The proposed method Results show that exudates were detected from a database with 96.9% sensitivity, specificity 96.1 % and 97.38%accuracy
Symbolic Analysis of Sonar Data for Underwater Target Detection
"... Abstract—This paper presents a symbolic pattern analysis method for robust feature extraction from sidescan sonar images that are generated from autonomous underwater vehicles (AUVs). The proposed data-driven algorithm, built upon the concepts of symbolic dynamics and automata theory, is used for de ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—This paper presents a symbolic pattern analysis method for robust feature extraction from sidescan sonar images that are generated from autonomous underwater vehicles (AUVs). The proposed data-driven algorithm, built upon the concepts of symbolic dynamics and automata theory, is used for detection of mines and mine-like objects in the undersea environment. This real-time algorithm is based on symbolization of the data space via coarse graining, i.e., partitioning of the two-dimensional sonar images. The statistical information, in terms of stochastic matrices that serve as features, is extracted from the symbolized images by construction of probabilistic finite state automata. A binary classifier is designed for discrimination of detected objects into mine-like and nonmine-like categories. The pattern analysis algorithm has been validated on sonar images generated in the exploration phase of a mine hunting operation; these data have been provided by the Naval Surface Warfare Center. The algo-rithm is formulated for real-time execution on limited-memory commercial-of-the-shelf platforms and is capable of detecting objects on the seabed-bottom. Index Terms—Feature extraction, mine countermeasures, pat-tern classification, sonar data analysis, symbolic dynamics, target detection. I.
