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There have been significant progresses in single image super-resolution (SR) using deep convolutional neural network. In this paper, we propose a modified deep convolutional neural network model incorporated with image texture priors for single image SR. The model consist of a particular feature extraction layer followed by image reconstruction process, aiming to centralize on the image texture information...
Person re-identification (Re-ID) remains a challenging problem due to significant appearance changes caused by variations in view angle, background clutter, illumination condition and mutual occlusion. To address these issues, conventional methods usually focus on proposing robust feature representation or learning metric transformation based on pairwise similarity, using Fisher-type criterion. The...
Recently, the sparse coding based image representation has achieved state-of-the-art recognition results on many benchmarks. In this paper, we propose Multi-cue Normalized Non-Negative Sparse Encoder (MN3SE) which enforces both the non-negative constraint and the shift-invariant constraint on top of the traditional sparse coding criteria, and takes multi-cue to further boost the performance. The former...
Recognizing continuous action composition in human behavior is an important and yet challenging problem. In this paper we tackle the task by developing both reliable image features and classification algorithms. For image features, we introduce the Embedded Optical Flow (EOF) feature based on embedding optical flow using Locality-constrained Linear Coding with weighted average pooling. The EOF feature...
The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected...
The paper attempts the recognition of multiple drivers' emotional state from physiological signals. The major challenge of the research is the severe inter-subject variation such that it is extreme difficult to build a general model for multiple drivers. In this paper, we focus on discovering an optimal feature mapping by utilizing the additional attribute from the drivers. Two models are reported,...
In this paper, we propose a face recognition approach using circularly symmetrical Gabor transform (CSGT). The traditional Gabor transform is replaced by CSGT. All the face images are transformed by CSGT first and then face recognition is performed in CSGT feature space. Detailed theoretical analysis is presented and simulation results on Yale, AR and ORL face databases are given. The recognition...
Semantic video content extraction and selection are critical steps in sports video analysis and editing. The identification of video segments can be from various semantic perspectives, e.g. certain event, player or emotional state. In this paper, we examined the possibility of automatically identifying shots with "happy" or "sad" emotion from broadcast sports video. Our proposed...
Building a generic content-based sports video analysis system remains a challenging problem because of the diversity in sports rules and game features which makes it difficult to discover generic low-level features or high-level modeling algorithms. One possible alternative is to first classify the sports genre and then apply specific sports domain knowledge to perform analysis. In this paper we describe...
Sports video highlight detection is a popular topic. A multi-layer sport event detection framework is described. In the mid-level of this framework, visual and audio keywords are created from low-level features and the original video is converted into a keyword sequence. In the high-level, the temporal pattern of keyword sequences is analyzed by an HMM classifier. The creation of visual and audio...
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