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Sequential learning-based pattern classification aims at providing more accurate labeled maps by adding an extra step of classification using an augmented feature vector. In this paper, we evaluated the robustness of Optimum-Path Forest (OPF) classifier in the context of land-cover classification using both satellite and radar images, showing OPF can benefit from sequential learning theoretical basis.
Detecting the text in natural scene images is often challenging due to the complexity and variety of text's appearance and its interaction with the scene context. In this paper, we present a novel hierarchical text detection method exploiting textual characteristics at both character and text line scales for improved accuracy. First, seed candidate characters are detected with discriminative deep...
Knowledge is one of the organization's most important values that influencing its competitiveness. One way to capture organization's knowledge and make it available to all their members is through the use of knowledge management systems. A critical process in the whole knowledge management life cycle is validation. A Knowledge Base (KB) incorporated into such systems has to be verified or (more generally)...
This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences...
In this paper, we present a randomized strategy for design under uncertainty. The main contribution is to provide a general class of sequential algorithms which satisfy the required specifications using probabilistic validation. At each iteration of the sequential algorithm, a candidate solution is probabilistically validated by means of a set of randomly generated uncertainty samples. The idea of...
Of various Human-Computer-Interactions (HCI), hand gesture based HCI might be the most natural and intuitive way to communicate between people and machines, since it closely mimics how human interact with each other. Its intuitiveness and naturalness have spawned many applications in exploring large and complex data, computer games, virtual reality, health care, etc. Although the market for hand gesture...
We present a novel and unique combination of algorithms to detect the gender of the leading vocalist in recorded popular music. Building on our previous successful approach that enhanced the harmonic parts by means of Non-Negative Matrix Factorization (NMF) for increased accuracy, we integrate on the one hand a new source separation algorithm specifically tailored to extracting the leading voice from...
High level context recognition and situation detection are enabling technologies for unobtrusive mobile computing systems. Significant progress has been made in processing and managing context information, leading to sophisticated frameworks, middlewares, and algorithms. Despite great improvements, context aware systems still require a significantly increased recognition accuracy for high-level context...
In the design of Classifier Ensembles, diversity is considered as one of the main aspects to be taken into account, since there is no gain in combining identical classification methods. One way of increasing diversity is to use feature selection methods in order to select subsets of attributes for the individual classifiers. In this paper, it is investigated the use of a simple reinforcement-based...
Current research of natural language steganographic algorithms based on synonymy substitution mostly focused on invisibility, but ignored robustness. However, automatic disambiguation of Chinese word senses (WSD) achieves high accuracy, the adversary could destroyed the watermark easily if he disambiguated the stego-text and did the synonym substitution again. In this paper, against the high accuracy...
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