351,684 research outputs found
Fast human detection for video event recognition
Human body detection, which has become a research hotspot during the last two years, can be used in many video content analysis applications. This paper investigates a fast human detection method for volume based video event detection. Compared with other object detection systems, human body detection brings more challenge due to threshold problems coming from a wide range of dynamic properties. Motivated by approaches successfully introduced in facial recognition applications, it adapts and adopts feature extraction and machine learning mechanism to classify certain areas from video frames. This method starts from the extraction of Haar-like features from large numbers of sample images for well-regulated feature distribution and is followed by AdaBoost learning and detection algorithm for pattern classification. Experiment on the classifier proves the Haar-like feature based machine learning mechanism can provide a fast and steady result for human body detection and can be further applied to reduce negative aspects in human modelling and analysis for volume based event detection
Randomized Dimensionality Reduction for k-means Clustering
We study the topic of dimensionality reduction for -means clustering.
Dimensionality reduction encompasses the union of two approaches: \emph{feature
selection} and \emph{feature extraction}. A feature selection based algorithm
for -means clustering selects a small subset of the input features and then
applies -means clustering on the selected features. A feature extraction
based algorithm for -means clustering constructs a small set of new
artificial features and then applies -means clustering on the constructed
features. Despite the significance of -means clustering as well as the
wealth of heuristic methods addressing it, provably accurate feature selection
methods for -means clustering are not known. On the other hand, two provably
accurate feature extraction methods for -means clustering are known in the
literature; one is based on random projections and the other is based on the
singular value decomposition (SVD).
This paper makes further progress towards a better understanding of
dimensionality reduction for -means clustering. Namely, we present the first
provably accurate feature selection method for -means clustering and, in
addition, we present two feature extraction methods. The first feature
extraction method is based on random projections and it improves upon the
existing results in terms of time complexity and number of features needed to
be extracted. The second feature extraction method is based on fast approximate
SVD factorizations and it also improves upon the existing results in terms of
time complexity. The proposed algorithms are randomized and provide
constant-factor approximation guarantees with respect to the optimal -means
objective value.Comment: IEEE Transactions on Information Theory, to appea
Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification
Unsupervised learning methods for feature extraction are becoming more and
more popular. We combine the popular contrastive learning method (prototypical
contrastive learning) and the classic representation learning method
(autoencoder) to design an unsupervised feature learning network for
hyperspectral classification. Experiments have proved that our two proposed
autoencoder networks have good feature learning capabilities by themselves, and
the contrastive learning network we designed can better combine the features of
the two to learn more representative features. As a result, our method
surpasses other comparison methods in the hyperspectral classification
experiments, including some supervised methods. Moreover, our method maintains
a fast feature extraction speed than baseline methods. In addition, our method
reduces the requirements for huge computing resources, separates feature
extraction and contrastive learning, and allows more researchers to conduct
research and experiments on unsupervised contrastive learning
Sparse Radial Sampling LBP for Writer Identification
In this paper we present the use of Sparse Radial Sampling Local Binary
Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture
classification. By adapting and extending the standard LBP operator to the
particularities of text we get a generic text-as-texture classification scheme
and apply it to writer identification. In experiments on CVL and ICDAR 2013
datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA)
performance. Among the SOA, the proposed method is the only one that is based
on dense extraction of a single local feature descriptor. This makes it fast
and applicable at the earliest stages in a DIA pipeline without the need for
segmentation, binarization, or extraction of multiple features.Comment: Submitted to the 13th International Conference on Document Analysis
and Recognition (ICDAR 2015
A fast image retrieval method designed for network big data
In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful
information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be
obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into
database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the
retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method
has a great improvement in the effective performance of feature extraction and can also get better search matching results
3D modelling and recognition
3D face recognition is an open field. In this paper we present a method for 3D facial recognition based on Principal Components Analysis. The method uses a relatively large number of facial measurements and ratios and yields reliable recognition. We also highlight our approach to sensor development for fast 3D model acquisition and automatic facial feature extraction
Source Separation using ICA
Independent Component Analysis (ICA) is a statistical signal processing technique having emerging new practical application areas, such as blind signal separation such as mixed voices or images, analysis of several types of data or feature extraction. Fast independent component analysis (Fast ICA ) is one of the most efficient ICA technique. Fast ICA algorithm separates the independent sources from their mixtures by measuring non-gaussianity.In this paper we present a method that can separate the signals as individual channels from other channels and also remove the noise using fast ica algorithm. The method is to decompose a multi channel signal into statistically independent components
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