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We propose a nuclear-norm regularized two-dimensional neighborhood preserving projection (2DNPP) for extracting representative 2D image features. Note that 2DNPP extracts neighborhood preserving features through minimizing the reconstruction error, but the Frobenius norm based metric is sensitive to noise and outliers. To make the distance metric more reliable and model the neighborhood reconstruction...
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Low-level feature encoding combined with Spatial Pyramid Matching (SPM) is widely adopted in the image classification system nowadays to extract features, which are usually high-dimensional. This not only makes the classification problem computationally prohibitive, but also raises other issues, such as the “curse of dimensionality”. In this paper we present supervised dimensionality reduction (DR)...
Approximation of the geometric features is an essential step in point cloud segmentation and surface reconstruction. Often, the planar surfaces are estimated using principal component analysis (PCA), which is sensitive to noise and smooths the sharp features. Hence, the segmentation results into unreliable reconstructed surfaces. This article presents a point cloud segmentation method for building...
Brain imaging data such as EEG or MEG is high-dimensional spatiotemporal measurements that commonly require dimensionality reduction before being used for further analysis or applications. This paper presents a new dimensionality reduction method based on the recent graph signal processing theory. Specifically, we focus on a task to classify the brain imaging signals recording the cortical activities...
Analysis procedures for higher-dimensional data are generally computationally costly; thereby justifying the high research interest in the area. Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of...
A personal or enterprise collection of a large set of face images may contain many types of tags used for querying the collection. Often the tags have many irrelevant content that may not reflect the image content in terms of the facial characteristics. In this paper, we propose a data curation method to filter out the irrelevant face images using a face recognition based subgraph identification....
The robust principal component analysis (PCA) method has shown very promising results in seismic ambient noise attenuation when dealing with outliers in the data. However, the model assumes a general Gaussian distribution plus sparse outliers for the noise. In seismic data however, the noise standard variation could vary from one place to another leading to a more heavy-tailed noise distribution....
Performing dimensionality reduction on features is essential in tackling a majority of large-scale computer vision and pattern recognition problems. The popularity of adopting high-dimensional descriptors has caused conventional techniques such as PCA inefficient or even unfeasible. We introduce an unsupervised deep-net approach, termed as recursive reduction net (RRN), to carrying out dimensionality...
Images are usually represented by different groups of features, such as color, shape and texture attributes. In this paper, we propose a classification approach that integrates multiple features, such as spectral and spatial information. We refer this approach to multiple feature learning via rotation (MFL-R) strategy, which adopt a rotation-based ensemble method by using a data transformation approach...
Convolutional Neural Network (CNN) based image representations have achieved high performance in image retrieval tasks. However, traditional CNN based global representations either provide high-dimensional features, which incurs large memory consumption and computing cost, or inadequately capture discriminative information in images, which degenerates the functionality of CNN features. To address...
Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as...
This paper presents a simple and efficient method for action recognition based on the learning of an explicit representation for an intrinsic dynamic shape manifold of human action. The proposed model relies on a short temporal set of FastMap dimensionality reduction-based technique for embedding a sequence of raw moving silhouettes, associated to an action video into a low-dimensional space, in order...
Recently, low-rank representation (LRR) based methods have been used for hyperspectral image (HSI) denoising, which can simultaneously remove different types of noise: Gaussian noise, impulse noise, dead lines, and so on. However, the LRR based method does not make full use of the spatial information in HSI. In this paper, we integrate the superpixel segmentation (SS) into the LRR, and propose a novel...
We present a novel statistical shape model and fitting process for the 3D Constrained Local Models (CLM), exploiting the properties of Independent Component Analysis (ICA), instead of the classic use of Principal Component Analysis (PCA), and adopting a non-Gaussian distribution of the shape prior information. Using ICA permits to exploit the real distribution of shape priors by adopting a Generalised...
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named Local Binary Pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) — one of the most...
Intrinsic image decomposition is an important topic in computer vision and computer graphics applications. However, this is a challenging problem by adopting the information of a single image. Therefore, additional priors or supplementary information such as multiply images or user interactions are necessary to address this problem. In this paper, we propose a novel scheme to use multiple images for...
In this paper, we present a novel unsupervised method for detecting outliers in image databases, when the images are misaligned by action of transformations forming a group. The main idea is that when the aligned data lie in a low dimensional subspace, the misaligned data, assuming that the group size is small, will lie in a low dimensional group-invariant subspace. We then explicitly exploit this...
The problem of person re-identification, identifying the same person appeared in different camera views, is an important and challenging task in computer vision that has high potential application in areas like visual surveillance. In this paper we introduce a new feature fusion strategy for person reidentification that combines low-level Weighted Histograms of Overlapping Stripes (WHOS) features...
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