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Nowadays the bag-of-visual-words is a very popular approach to perform the task of Visual Object Classification (VOC). Two key phases of VOC are the vocabulary building step, i.e. the construction of a ‘visual dictionary’ including common codewords in the image corpus, and the assignment step, i.e. the encoding of the images by means of these codewords. Hard assignment of image descriptors to visual...
We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient...
In this paper, we present a new single shot structured light method to recover dense depth maps. Contrary to most temporal coding methods which require projecting a series of patterns, our method needs one color pattern only. Unlike most single shot spatial coding methods which establish correspondence along the edges of the captured images, our method produces a dense set of correspondences. Our...
In object recognition a robust feature set is considered as an important component in almost all the approaches proposed in the literature. In facial analysis, one of the best known feature set is based in Local Binary Patterns (LBP) which extracts the information contained in the image using comparisons between pixels in a region, finally such comparisons are encoded in form of histogram. We argue...
Event recognition has been an important topic in computer vision research due to its many applications. However, most of the work has focused on videos taken from a fixed camera, known environments and basic events. Here, we focus on classification of unconstrained, web videos into much higher level activities. We follow the approach of constructing fixed length feature vectors from local feature...
This paper presents the lessons learnt on the design, development and evaluation of a pervasive computing-based system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years of activity within the MONARCA EU project. The challenges listed and detailed in this paper may be used in future research as a set of relevant checklist items in the development...
This paper addresses the challenging problem of scene classification in street-view georeferenced images of urban environments. More precisely, the goal of this task is semantic image classification, consisting in predicting in a given image, the presence or absence of a pre-defined class (e.g. shops, vegetation, etc.). The approach is based on the BOSSA representation, which enriches the Bag of Words...
This paper introduces a novel coding scheme based on the diffusion map framework. The idea is to run a t-step random walk on the data graph to capture the similarity of a data point to the codebook atoms. By doing this we exploit local similarities extracted from the data structure to obtain a global similarity which takes into account the non-linear structure of the data. Unlike the locality-based...
Video denoising based on temporal or spatiotemporal filtering is highly effective but computationally expensive due to the requirement of motion estimation. Encoder-integrated denoising is an efficient framework that embeds the filtering process into the encoding pipeline so that motion estimation for denoising can be avoided. State-of-the-arts encoder-integrated methods use Least Minimum Mean Square...
TCP is the ubiquitous transport protocol in the Internet. However, in a wireless ad-hoc environment where links are unreliable, TCP causes a number of performance issues. The key reason behind this is that TCP considers all packet losses to be due to congestion and reduces its send rate, which is not necessarily appropriate in a lossy ad-hoc environment. In prior work, we have designed Loss Tolerant...
Unsupervised clustering of large data sets is a complicated NP-hard task. Due to its complexity, various metaheuristic machine learning algorithms have been used to automate or aid the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. However, also evolutionary clustering suffers from the high computational demands when it comes...
Neighborhood coding is a binary image representation method that has been performing successfully for a variety of applications such as features extraction for image recognition, shape descriptor, and image compression. Despite the success of this coding method, the representation lacked a formal notation. Here, we proposed a formal mathematical notation to represent any binary image into a neighborhood...
Lack of efficient and transparent interaction with GPU data in hybrid MPI+GPU environments challenges GPU acceleration of large-scale scientific computations. A particular challenge is the transfer of noncontiguous data to and from GPU memory. MPI implementations currently do not provide an efficient means of utilizing data types for noncontiguous communication of data in GPU memory. To address this...
The exponential growth in user and application data entails new means for providing fault tolerance and protection against data loss. High Performance Computing (HPC) storage systems, which are at the forefront of handling the data deluge, typically employ hardware RAID at the backend. However, such solutions are costly, do not ensure end-to-end data integrity, and can become a bottleneck during data...
On one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders...
The Bag-of-Features (BOF) model is widely used for image classification. Most BOF models incorporate a step of maximum pooling to generate the raw image representation, where salient atoms with maximum response are reserved for final representation. However, recent locality-preserving coding schemes do not account for the saliency characteristic during the process of generating the raw image representations...
Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance,...
Efficient learning with non-linear kernels is often based on extracting features from the data that “linearise” the kernel. While most constructions aim at obtaining low-dimensional and dense features, in this work we explore high-dimensional and sparse ones. We give a method to compute sparse features for arbitrary kernels, re-deriving as a special case a popular map for the intersection kernel and...
In this work, we introduce a hierarchical matching framework with so-called side information for image classification based on bag-of-words representation. Each image is expressed as a bag of orderless pairs, each of which includes a local feature vector encoded over a visual dictionary, and its corresponding side information from priors or contexts. The side information is used for hierarchical clustering...
A brute-force algorithm to solve small instances of the Dominating Set Problem on GPUs is presented. Two implementations of the algorithm are discussed, one that uses atomic operations and one that uses reductions. Experimental results are reported.
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