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We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical object representation algorithm, namely Learned Hierarchy of Parts (LHOP) [7]. In the proposed approach, we first employ the LHOP to learn hierarchical part libraries which represent...
Parallel skeletons provide a predefined set of parallel templates that can be combined, nested and parameterized with sequential code to produce complex parallel programs. The implementation of each skeleton includes parameters that have a significant effect on performance; so carefully tuning them is vital. The optimization space formed by these parameters is complex, non-linear, exhibits multiple...
Using machine learning has proven effective at choosing the right set of optimizations for a particular program. For machine learning techniques to be most effective, compiler writers have to develop expressive means of characterizing the program being optimized. The start-of-art techniques for characterizing programs include using a fixed-length feature vector of either source code features extracted...
Mention detection is an important component in anaphora resolution. In this paper we present our works on mention detection based on differential evolution (DE). The proposed technique consists of two steps, viz. feature selection and classifier ensemble. In the first step the algorithm performs automatic feature selection for two machine learning algorithms, namely Conditional Random Field (CRF)...
Scale-invariant feature transform (SIFT) based feature extraction algorithm is widely applied to extract features from images, and it is very attractive to accelerate these SIFT based algorithms on GPU. In this paper, we present several parallel computing strategies, implement and optimize the SIFT algorithm using CUDA programming model on GPU. Each stage of SIFT is analyzed in detail to choose the...
Clustering-based Discriminant Analysis (CDA) is a well-known technique for supervised feature extraction and dimensionality reduction. CDA determines an optimal discriminant subspace for linear data projection based on the assumptions of normal subclass distributions and subclass representation by using the mean subclass vector. However, in several cases, there might be other subclass representative...
This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The...
Massive amounts of textual and digital data are created daily from business or public activities. The organisation, mining and summarization of such a rich and large information source is required to capture the essential and critical knowledge it contains. Such a mining is of strategic importance in many domains including innovation (eg to mine technological reviews and scientific literature) and...
Image search reranking has attracted extensive attention. However, existing image reranking approaches deal with different features independently while ignoring the latent topics among them. It is important to mine multi-latent topic from the features to solve the image search reranking problem. In this paper, we propose a new image reranking model, named reranking with multi-latent topical graph...
With the rapid growth of technology the machines has to realize the information by adapting to the internal information. Due to potential growth of multimedia hardware and applications, the information retrieval has been analyzed by content based image retrieval (CBIR). Feature extraction has been done with the Euclidean distance estimation between the pixels; relevance feedback (RF) based approach...
One of the principal goals for most research scientists is to publish. There are many thousands of publications: journals, conferences, workshops, and more, covering different topics and requiring different writing formats. However, when a researcher that is new to a certain research domain finishes the work, it is sometimes difficult to find a proper place to submit the paper. To solve this problem,...
In this article the text-independent speaker verification problem is considered. In the presented system each conversation side is represented as a vector lying on the unit hypersphere. These vectors are compared by an inner product which produces similarity scores. In this article classical score normalization methods (z-norm and t-norm) are analyzed and compared with the support vector machines...
We use contextual constraints for person retrieval in camera networks. We start by formulating a set of general positive and negative constraints on the identities of person tracks in camera networks, such as a person cannot appear twice in the same frame. We then show how these constraints can be used to improve person retrieval. First, we use the constraints to obtain training data in an unsupervised...
The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize...
Support vector data description (SVDD) has been widely used in pattern classification, however it does not provide high performance in brain-computer interface (BCI) classification problems since brain signals are noisy and chaotic. Brain data have distinct distributions and hence a hyper-sphere in SVDD could not well describe the data. We propose in this paper a multi-sphere approach to SVDD to have...
This paper proposes a novel methodology for the optimal and simultaneous designs of both Hermitian transforms and masks for reducing the intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images. Each class of training images associates with a Hermitian transform, a mask and a known represented feature vector. The optimal and simultaneous designs of both the Hermitian...
In this paper, we study the problem of robust feature extraction based on l2, 1 regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an l2, 1-norm minimization can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates convergence study and algorithmic development. In particular, a general formulation is accordingly...
Recent work has demonstrated the effectiveness of domain adaptation methods for computer vision applications. In this work, we propose a new multiple source domain adaptation method called Domain Selection Machine (DSM) for event recognition in consumer videos by leveraging a large number of loosely labeled web images from different sources (e.g., Flickr.com and Photosig.com), in which there are no...
A novel presentation for channel selection problem in Brain-Computer Interfaces (BCI) is introduced here. Continuous presentation in a projected two-dimensional space of the Electroencephalograph (EEG) cap is proposed. A multi-objective particle swarm optimization method (D2MOPSO) is employed where particles move in the EEG cap space to locate the optimum set of solutions that minimize the number...
Improving coding and spatial pooling for bag-of-words based feature design have gained a lot of attention in recent works addressing object recognition and scene classification. Regarding the coding step in particular, properties such as sparsity, locality and saliency have been investigated. The main contribution of this work consists in taking into acount the local spatial context of an image into...
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