Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.

Synthesis Analysis Methods for Underwater Video Compression with Tensor Based Minimized Side Information

Synthesis analysis is a common approach used to compress videos with more amounts of dynamic textures. Underwater videos contain more moving species captured by moving camera. These kinds of videos have two types of motion registered by both the species and the camera. In this paper, tensor, an N-way representation of data is used to store the side information obtained from the synthesis analysis approach. The Low multilinear rank approximation (LMLRA) with error correction using residual tensor is applied on the side information to reduce the memory space for side information. The host encoder in synthesis analysis approach plays an important role in providing high compression rate with minimal loss and hence H.265 is used as the host encoder. The results show that the proposed method achieves highest compression ratio with minimal loss due to distortion and saved bit rate which is highly consumed by dynamic textures.

Learning Dynamic Generator Model by Alternating Back-Propagation through Time

This paper studies the dynamic generator model for spatialtemporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the video sequence is generated by a generator model, which is a non-linear transformation of a latent state vector, where the non-linear transformation is parametrized by a top-down neural network. The sequence of latent state vectors follows a non-linear auto-regressive model, where the state vector of the next frame is a non-linear transformation of the state vector of the current frame as well as an independent noise vector that provides randomness in the transition. The non-linear transformation of this transition model can be parametrized by a feedforward neural network. We show that this model can be learned by an alternating back-propagation through time algorithm that iteratively samples the noise vectors and updates the parameters in the transition model and the generator model. We show that our training method can learn realistic models for dynamic textures and action patterns.

High Order Volumetric Directional Pattern for Video-Based Face Recognition

Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches.