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Semi-supervised extreme learning machine (SSELM) was proposed as an effective algorithm for machine learning and pattern recognition. However, the performance of SSELM heavily depends on whether the underlying geometrical structure of the data can be well exploited. Though many techniques have been utilized for constructing graph to represent the data structure, which of them can best reflect the...
Feature selection, which aims to select the most informative feature subset, has been playing a critical role in dimension reduction. In this paper, a novel unsupervised feature selection algorithm called the inner product regularized nonnegative self-representation (IRNSR) is designed for image classification and clustering. In the IRNSR algorithm, first, each feature in high-dimensional data is...
The planted acreage estimation for major crops by using remote sensing is typically combine the sample data from ground survey with information derived from image classification, and the common applied approaches are regression estimator by using linear model and calibration estimator by using confusion matrix. In general, the crop acreage estimation for provincial level in China only satisfied the...
In this paper, we propose a new method for modeling appearance variances in generic object tracking task. Although object tracking has been studied by many researchers for a long time, there are still many challenging problems, which is mainly due to the complex variances of object's appearance. While most of traditional methods using a global or pixel-wise approach, we proposed a part-based tracking...
This paper presents a novel algorithm for face recognition. Local Quaternion Patters (LQP) is proposed for presenting the feature parts in the face. To keep the spatial feature of the face, an asymmetric similarity measure Weighted Spatially constrained Earth Mover's Distance (WSEMD) is studied for classification. In this step, the source image is partitioned into non overlapping local patches while...
Object recognition based on probabilistic Latent Semantic Analysis (pLSA) has shown excellent performance, but it is sensitive to background clutter. In this paper, we propose a novel framework called AM-pLSA, which combines pLSA with visual attention model, to learn object classes from unlabeled images with cluttered background. We firstly detect salient regions and non-salient regions in an image...
One of the main drawbacks of boosting is its overfitting and poor predictive accuracy when the training dataset is small and imbalanced. In this paper, we introduce a novel learning algorithm Boost-BFKO, which combines boosting and data generation. It is suitable for small and imbalanced training datasets. To enlarge training sets, Boost-BFKO uses the adaptive Balanced Feature Knockout procedure (BFKO)...
In this paper, multi-classifier system (MCS) is applied to the automatic classification of remote sensing images, and some effective multi-classifier fusion methods with relatively high accuracy are proposed based on substantive experiments. The classification accuracy of MCS has been remarkably improved compared to single classifier with an average increment of 5%. In addition, a diversity measure...
In order to improve accuracy of image Segmentation, a new merging method based on Bayesian classifier is proposed for the medical image Segmentation. There are many particles such as red blood cells, white blood cells, pipe type cells, epitheliums and the crystallizations in urinary sediment images. Segmentation the various elements among the particles is very important to medical decision. Existing...
Airbag deployments during automobile accidents can cause injury and even death if the occupant is an improper size or in an improper position. Because of this, steps have been taken to make airbag deployment safer by adding adaptive deployment decision capabilities. This paper presents a new method to obtain occupants' pose and location by using a CCD camera fixed in a cabin. The face location is...
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