Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

A two level approach for scene recognition

Abstract: Classifying pictures into one of several semantic categories is a classical image understanding problem. In this paper, we present a stratified approach to both binary (outdoor-indoor) and multiple category of scene classification. We first learn mixture models for 20 basic classes of local image content based on color and texture information. Once trained, these models are applied to a test image, and produce 20 probability density response maps (PDRM) indicating the likelihood that each image region was produced by each class. We then extract some very simple features from those PDRMs, and use them to train a bagged LDA classifier for 10 scene categories. For this process, no explicit region segmentation or spatial context model are computed. To test this classification system, we created a labeled database of 1500 photos taken under very different environment and lighting conditions, using different cameras, and from 43 persons over 5 years. The classification rate of outdoor-indoor classification is 93.8%, and the classification rate for 10 scene categories is 90.1%. As a byproduct, local image patches can be contextually labeled into the 20 basic material classes by using loopy belief propagation (Yedidia et al., 2001) as an anisotropic filter on PDRMs, producing an image-level segmentation if desired.

An Efficient and Robust Framework for SAR Target Recognition by Hierarchically Fusing Global and Local Features

Abstract: Automatic target recognition (ATR) of synthetic aperture radar (SAR) images is performed on either global or local features. The global features can be extracted and classified with high efficiency. However, they lack the reasoning capability thus can hardly work well under the extended operation conditions (EOCs). The local features are often more difficult to extract and classify but they provide reasoning capability for EOC target recognition. To combine the efficiency and robustness in an ATR system, a hierarchical fusion of the global and local features is proposed for SAR ATR in this paper. As the global features, the random projection features can be efficiently extracted and effectively classified by sparse representation-based classification (SRC). The physically relevant local descriptors, i.e., attributed scattering centers (ASCs), are employed for local reasoning to handle various EOCs like noise corruption, resolution variance, and partial occlusion. A one-to-one correspondence between the test and template ASC sets is built by the Hungarian algorithm. Then, the local reasoning is performed by evaluating individual matched pairs as well as the false alarms and missing alarms. For the test image to be recognized, it is first classified by the global classifier, i.e., SRC. Once a reliable decision is made, the whole recognition process terminates. When the decision is not reliable enough, it is passed to the local classifier, where a further classification by ASC matching is carried out. Therefore, by the hierarchical fusion strategy, the efficiency of global features and the robustness of local descriptors to various EOCs can be maintained jointly in the ATR system. Extensive experiments on the moving and stationary target acquisition and recognition data set demonstrate that the proposed method achieves superior effectiveness and robustness under both SOC and typical EOCs, i.e., noise corruption, resolution variance, and partial occlusion, compared with some other SAR ATR methods.

Object-oriented classification of polarimetric SAR imagery based on Statistical Region Merging and Support Vector Machine

Abstract: This paper presents a new object-oriented classification method based on statistical region merging (SRM) for segmentation and support vector machine (SVM) for classification where polarimetric synthetic aperture radar (PolSAR) data are used. The proposed approach makes use of polarimetric information of PolSAR data, and takes advantage of SRM and SVM. The SRM segmentation method not only considers spectral, shape, scale information, but also has the ability to cope with significant noise corruption, handle occlusions. The SVM used for classification takes its advantages of solving sparse sampling, non-linear, high-dimensional, and global optimum problems comparing with other classifiers. It is thus expected that the input vectors of SVM will include fully polarimetric information for image classification. A test image, acquired by the Jet Propulsion Laboratory Airborne SAR (AIRSAR) system, is used to demonstrate the advantages of the proposed method. It is shown that the proposed approach outperforms the traditional pixel-based SVM classification method for land cover classification with PolSAR data, and the integration of SRM and SVM makes the proposed algorithm an attractive and alternative method for polarimetric SAR classification.

Recognition by parts using adaptive and robust correlation filters

Abstract: A recognition-by-parts authentication system for determining if a physical test target represented in test image(s) obtained using an imaging device matches a physical training target represented in training image(s). The system includes a multitude of adaptive and robust correlation filters. Each of the adaptive and robust correlation filters is configured to generate correlation-peak-strength and distance-from-origin data using a multitude of related images. Each of the multitude of related images representing a similar part of a larger image. The related images originate from the test image(s) and training image(s).