In this paper, the problem of multi-view embedding from different visual cues and modalities is considered. We propose a unified solution for subspace learning methods using the Rayleigh quotient, which is extensible for multiple views, supervised learning, and nonlinear embeddings. Numerous methods including canonical correlation analysis, partial least square regression, and linear discrimina...

In this paper, a novel subspace learning method called neighborhood discriminant embedding (NDE) is proposed for pattern classification. In our algorithm, the neighbor and class relations of training samples data are used to construct the low-dimensional embedding submanifold. After being embedding into a low-dimensional subspace, in a local structure, samples from the same class will be as clo...

Abstract   Recent developments in robotics automation have motivated researchers to improve the efficiency of interactive systems by making a natural man-machine interaction. Since speech is the most popular method of communication, recognizing human emotions from speech signal becomes a challenging research topic known as Speech Emotion Recognition (SER). In this study, we propose a Persian em...

Manifold learning is an efficient approach for recognizing human actions. Most of the previous embedding methods are learned based on the distances between frames as data points. Thus they may be efficient in the frame recognition framework, but they will not guarantee to give optimum results when sequences are to be classified as in the case of action recognition in which temporal constraints ...

In this paper, we propose a new supervised linear dimensionality reduction method called discriminant projection embedding (DPE). DPE can preserve within-class neighboring geometry and extract between-class relevant structures for classification effectively. The proposed method is applied to face and palmprint recognition and is examined using the AR and FERET face databases and the PolyU palmp...

In a closed 2-cell embedding of a graph each face is homeomorphic to an open disk and is bounded by a cycle in the graph. The Orientable Strong Embedding Conjecture says that every 2-connected graph has a closed 2-cell embedding in some orientable surface. This implies both the Cycle Double Cover Conjecture and the Strong Embedding Conjecture. In this paper we prove that every 2-connected proje...

We propose a secure steganographic approach with a high capacity. We first use small neighborhoods of the original histogram to calculate the minimum remapping from a histogram to the odd-only and even-only histograms. We then employ a quantization index modulation-based odd-even embedding to preserve the histogram of each embedding subchannel. A minimum distortion remapping policy is used to d...