Hybrid Approach for Detection of Objects from Images Using Fisher Vector and PSO Based CNN

Owing to the near connection between object recognition and video processing and picture perception, a lot of research interest has been received in recent years. Standard methods of object detection are focused on manufactured technologies and slow-moving architectures. Fisher Vectors (FV) and Convolutional Neural Networks (CNN) are two picture arrangement pipelines with various qualities. While CNNs have indicated predominant exactness on various order assignments, FV classifiers are normally less exorbitant to prepare and assess. In this paper we propose a mechanism for detection of objects in image based on Fisher kernel and CNN with a PSO optimization technique. Here fisher kernel draws the global or statically features from the image object and CNN is used for local and more complex feature extraction from an image and here we use CNN with PSO to reduce the training complexity. Performance results shows that the proposed model is detect the object better than the existing models.

Affective actions recognition in dyadic interactions based on generative and discriminative models

Purpose Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions. Design/methodology/approach A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation). Findings Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN. Practical implications The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities. Originality/value The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

Application of Support Vector Machine (SVM) in the Sentiment Analysis of Twitter DataSet

At present, in the mainstream sentiment analysis methods represented by the Support Vector Machine, the vocabulary and the latent semantic information involved in the text are not well considered, and sentiment analysis of text is dependent overly on the statistics of sentiment words. Thus, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed in this paper for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived from the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information involving the probability characteristics can be used as the classification characteristics, along with the improvement of the effect of classification for support vector machine, and the problem of ignoring the latent semantic characteristics in text sentiment analysis can be addressed. The results show that the effect of the method proposed in this paper, compared with the comparison method, is obviously improved.

Bayesian network Fisher kernel for categorical feature spaces

We address the problem of defining similarity between vectors of possibly dependent categorical variables by deriving formulas for the Fisher kernel for Bayesian networks. While both Bayesian networks and Fisher kernels are established techniques, this result does not seem to appear in the literature. Such a kernel naturally opens up the possibility to conduct kernel-based analyses in completely categorical feature spaces with dependent features. We show experimentally how this kernel can be used to find subsets of observations that we see as representative for the underlying Bayesian network model.