Learning correlations for human action recognition in videos

Multimedia Tools and Applications - Human action recognition in realistic videos is an important and challenging task. Recent studies demonstrate that multi-feature fusion can significantly improve...     

Deep metric learning for open-set human action recognition in videos

Neural Computing and Applications - Human action recognition (HAR) is a topic widely studied in computer vision and pattern recognition. Despite the success of recent models for this issue, most of...     

An efficient approach for face recognition based on common eigenvalues

In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination.     

Second-order motion descriptors for efficient action recognition

Pattern Analysis and Applications - Human action recognition from realistic video data constitutes a challenging and relevant research area. Leading the state of the art we can find those methods...     

An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.     

(2D)LDA: An efficient approach for face recognition

Although 2DLDA algorithm obtains higher recognition accuracy, a vital unresolved problem of 2DLDA is that it needs huge feature matrix for the task of face recognition. To overcome this problem, this paper presents an efficient approach for face image feature extraction, namely, (2D)²LDA method. Experimental results on ORL and Yale database show that the proposed method obtains good recognition accuracy despite having less number of coefficients.     

Human action recognition in videos based on the Transferable Belief Model

This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic world of human interpretation. For that, a fusion architecture based on the Transferable Belief Model framework is proposed and applied to action recognition of an athlete in video sequences of athletics meeting with moving camera. Relevant features are extracted from videos, based on both the camera motion analysis and the tracking of particular points on the athlete’s silhouette. Some models of interpretation are used to link the numerical features to the symbols to be recognized, which are running, jumping and falling actions. A Temporal Belief Filter is then used to improve the robustness of action recognition. The proposed approach demonstrates good performance when tested on real videos of athletics sports videos (high jumps, pole vaults, triple jumps and long jumps) acquired by a moving camera and different view angles. The proposed system is also compared to Bayesian Networks.

An efficient CNN based encrypted Iris recognition approach in cognitive-IoT system

Multimedia Tools and Applications - Recently, biometric-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT) based security framework. The iris trait solves...     

An efficient labeling technique for solving sparse assignment problems

We describe a new implementation of the shortest augmenting path approach for solving sparse assignment problems and report computational experience documenting its efficiency.     

Linear dynamical systems approach for human action recognition with dual-stream deep features

Human action recognition with a dual-stream architecture using linear dynamical systems (LDSs) approach is discussed in this paper. First, a slice process is established to extract original slices from video sequences. Two slicing methods are adopted to subtract or reserve the remaining frames in the video sequences. By applying background subtraction to adjacent frames of the original slices, difference slices are also expressed. To capture the spatial component of the background and difference expressed in each slice simultaneously, a framework based on pre-trained convolutional neural networks (CNNs) is introduced for dual-stream deep feature extraction. Subsequently, LDSs are established to model the timing relationship between adjacent slices and obtain the temporal component of the background and difference features, which are expressed as linear dynamical background feature (LD-BF) and linear dynamical difference feature (LD-DF). Practical experiments were conducted to demonstrate the effectiveness and robustness of the proposed approach using different datasets. Specifically, our experiments were conducted on the UCF50, UCF101, and hmdb51 datasets. The impact of retaining various principal component analysis (PCA) feature dimensions and distinct slicing methods in terms of detail recognition were evaluated. In particular, combining LD-BF with LD-DF under appropriate feature dimensions and slicing methods further improved the accuracy for the UCF50, UCF101, and hmdb51 datasets. In addition, the computational cost of the feature extraction process was evaluated to illustrate the efficiency of the proposed approach. The experimental results show that the proposed approach is competitive with state-of-the-art approaches in the three datasets.

     

Hierarchical BoW with segmental sparse coding for large scale image classification and retrieval

Multimedia Tools and Applications - The bag-of-words (BoW) has been widely regarded as the most successful algorithms for content-based image related tasks, such as large scale image retrieval,...     

Smile intensity recognition in real time videos: fuzzy system approach

Multimedia Tools and Applications - Facial emotion is a significant way of understanding or interpreting one’s inner thoughts. Real time video at any instant exhibits the emotion which serves...     

Recognizing 50 human action categories of web videos

Action recognition on large categories of unconstrained videos taken from the web is a very challenging problem compared to datasets like KTH (6 actions), IXMAS (13 actions), and Weizmann (10 actions). Challenges like camera motion, different viewpoints, large interclass variations, cluttered background, occlusions, bad illumination conditions, and poor quality of web videos cause the majority of the state-of-the-art action recognition approaches to fail. Also, an increased number of categories and the inclusion of actions with high confusion add to the challenges. In this paper, we propose using the scene context information obtained from moving and stationary pixels in the key frames, in conjunction with motion features, to solve the action recognition problem on a large (50 actions) dataset with videos from the web. We perform a combination of early and late fusion on multiple features to handle the very large number of categories. We demonstrate that scene context is a very important feature to perform action recognition on very large datasets. The proposed method does not require any kind of video stabilization, person detection, or tracking and pruning of features. Our approach gives good performance on a large number of action categories; it has been tested on the UCF50 dataset with 50 action categories, which is an extension of the UCF YouTube Action (UCF11) dataset containing 11 action categories. We also tested our approach on the KTH and HMDB51 datasets for comparison.     

Multi-modality learning for human action recognition

Multimedia Tools and Applications - The multi-modality based human action recognition is an increasing topic. Multi-modality can provide more abundant and complementary information than single...     

An efficient 3D face recognition approach using local geometrical signatures

This paper presents a computationally efficient 3D face recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the face. Kernel Principal Component Analysis (KPCA) is then used to extract the mid-level features from the extracted ARSs to improve the discriminative power. The mid-level features are then concatenated into a single feature vector and fed into a Support Vector Machine (SVM) to perform face recognition. The proposed approach addresses the expression variation problem by using facial scans with various expressions of different individuals for training. We conducted a number of experiments on the Face Recognition Grand Challenge (FRGC v2.0) and the 3D track of Shape Retrieval Contest (SHREC 2008) datasets, and a superior recognition performance has been achieved. Our experimental results show that the proposed system achieves very high Verification Rates (VRs) of 97.8% and 88.5% at a 0.1% False Acceptance Rate (FAR) for the “neutral vs. nonneutral” experiments on the FRGC v2.0 and the SHREC 2008 datasets respectively, and 96.7% for the ROC III experiment of the FRGC v2.0 dataset. Our experiments also demonstrate the computational efficiency of the proposed approach.