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This paper proposes two methods for solving the expert finding problem. In order to enhance the correctness, a C-value method is applied to these methods for query expansion. After query expansion, proposed system calculates correlation between all query terms and experts, and finally outputs a list of experts. The experiment results show that the proposed methods can provide higher precision than...
One of the fast similarity search techniques is a binary hashing method that transforms a real-valued vector into a binary code. The similarity between two binary codes is measured by their Hamming distance. In this method, a hash table is often used for realizing the constant time similarity search. The number of accesses to the hash table, however, increases when the number of bits becomes long...
During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that might result. This paper proposes an automated computer vision solution for detecting high-stakes deception based on facial clues. Four deceptive cues (eye-blink, eyebrow motion, wrinkle occurrence and mouth motion) were identified and integrated into a single facial behavior pattern vector...
Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial...
In this paper, a refined template selection algorithm was proposed for NR-based image feature extraction. The proposed method was devoted to reduce the computation complexity and to improve the recognition ability. Experimental results on several image databases strongly demonstrated the efficiency and effectiveness of the proposed method.
In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a hybrid approach. There is an absence of a study focused on offline features for handwritten math symbol recognition. Furthermore, many papers provide results difficult to compare. In this...
This work proposes a low cost alternative for DSP kits for image processing. Here we used friendly arm board with opencv. Friendly arm board is a low cost embedded board with ARM9 as the core where as Opencv is a computer vision simulation library from Intel. In this work the platform for image processing is prepared by porting the opencv library to the ARM9 board. Two algorithms for face recognition...
In order to perform the carrying task of large and heavy objects, the robot needs to acquire the proper manipulation strategy depending on the object property. The manipulation strategy which depends on unknown physical parameters such as mass and friction property is difficult to plan beforehand, so acquiring strategy by trial and error is effective. In this paper, we propose the learning methods...
In this paper we introduce a novel method for movement recognition in motion capture data. A movement is regarded as a combination of basic movement patterns, the so-called dynemes. Initially a K-means variant that takes into account the periodic nature of angular data is applied on training data to discover the most discriminative dynemes. Each frame is then assigned to one of these dynemes and a...
In this paper, we present a novel face recognition approach using 3D directional corner points (3D DCPs). Traditionally, points and meshes are applied to represent and match 3D shapes. Here we represent 3D surfaces by 3D DCPs derived from ridge and valley curves. Then we develop a 3D DCP matching method to compute the similarity of two different 3D surfaces. This representation, along with the similarity...
This paper presents a thorough study into the influence of the image resolution on automatic face gender classification. The images involved range from extremely low resolutions (2 ? 1 pixels) to full face sizes (329 ? 264 pixels). A comprehensive comparison of the performances achieved by two classifiers using ten different image sizes is provided by means of two performance measures Correct Classification...
In this paper, we propose a novel algorithm for Single-hidden Layer Feed forward Neural networks training which is able to exploit information coming from both labeled and unlabeled data for semi-supervised action classification. We extend the Extreme Learning Machine algorithm by incorporating appropriate regularization terms describing geometric properties and discrimination criteria of the training...
We propose in this paper a framework for the segmentation and classification of document streams. The framework is composed of two modules: segmentation and verification. The two modules use an incremental classifier which learns progressively along the stream. In the segmentation module a relationship between two consecutive pages is classified as either: continuity or rupture. Rupture is synonymous...
Scale invariant texture analysis is a fundamental challenge in image processing. As a consequence of the scale invariance, these kind of features are often characterized by a lower discriminative power. We observed, that scale invariant features did not pose a benefit in classification scenarios with varying scales in the training set. This is supposed to be an effect caused by an implicit scale selection...
Spotting micro-expressions is a primary step for continuous emotion recognition from videos. Spotting in this context refers to automatically finding the temporal locations of the face-related events from a video sequence. Rapid facial movements mainly include micro-expressions and eye blinks. However, the role of eye blinks in expressing emotions is still controversial, and often they are considered...
Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computationally costly and sometimes infeasible when the dataset size is massive and non-stationary. In this paper, we propose an incremental learning approach that greatly reduces the time...
The need for efficient tools to index and retrieve 3D content becomes even more acute. This paper presents a fully automatic 3D-object retrieval method. It consists of two main steps namely shape signature extraction to describe the shape of objects, and similarity computing to compute similarity between objects. In the first step (signature extraction), we use a shape descriptor called geodesic cords...
Occurrence of high imbalance in real-world domains is a direct result of rarity of interesting events, which results in skewed datasets. Without dataset rebalancing, the learning algorithm will encounter extremely low minority class samples therefore it gets biased towards the majority class in the classification tasks. Hence properly handling the imbalanced dataset is a crucial issue in the pattern...
Numerous fields require large-scale pattern matching to achieve a variety of computational goals. Herein, we present novel graphics processing unit (GPU) extensions that facilitate high-throughput pattern matching in a PostgreSQL database. We have developed an extension framework to perform data block processing of large pattern data sets, using a stream processing design that results in global k-nearest...
In this paper, we develop a new efficient graph construction algorithm that is useful for many learning tasks. Unlike the main stream for graph construction, our proposed data self-representativeness approach simultaneously estimates the graph structure and its edge weights through sample coding. Compared with the recent l1 graph that is based on sparse coding, our proposed objective function has...
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