Abstract Visual Surveillance systems have greatly increased in past few years. Several methods have been proposed in order to improve the efficiency of Face Detection but still remains a challenging task due to various illumination, poses and occlusion conditions. In this paper, we propose a novel method for Face Detection where a decision boundary is defined for skin classifier based on training dataset. Log-Gabor... Visual Surveillance systems have greatly increased in past few years. Several methods have been proposed in order to improve the efficiency of Face Detection but still remains a challenging task due to various illumination, poses and occlusion conditions. In this paper, we propose a novel method for Face Detection where a decision boundary is defined for skin classifier based on training dataset. Log-Gabor filter is used for feature extraction which is superior to Gabor filter as they can represent better frequency properties of the objects present in the video and SVM classifier is used for classifying it as face or non-face. The proposed method is tested on standard and our own collected video sequences, which shows good tolerance and is better than those of existing related algorithms. Read More Read Less

Abstract Content Based Image Retrieval (CBIR) systems use Relevance Feedback (RF) in order to improve the retrieval accuracy. Research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. In this paper, a novel system is proposed to enhance the gain of long-term relevance feedback. In the proposed... Content Based Image Retrieval (CBIR) systems use Relevance Feedback (RF) in order to improve the retrieval accuracy. Research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. In this paper, a novel system is proposed to enhance the gain of long-term relevance feedback. In the proposed system, the general CBIR involves two steps- ABC based training and image retrieval. First, the images other than the query image are pre-processed using median filter and gray scale transformation for removal of noise and resizing. Secondly, the features such as Color, Texture and shape of the image are extracted using Gabor Filter, Gray Level Co-occurrence Matrix and Hu-Moment shape feature techniques and also extract the static features like mean and standard deviation. The extracted features are clustered using k-means algorithm and each cluster are trained using ANN based ABC technique. A method using artificial bee colony (ABC) based artificial neural network (ANN) to update the weights assigned to features by accumulating the knowledge obtained from the user over iterations. Eventually, the comparative analysis performed using the commonly used methods namely precision and recall were clearly shown that the proposed system is suitable for the better CBIR and it can reduce the semantic gap than the conventional systems. Read More Read Less

Abstract Block motion estimation and compensation are played a major role in the video compression to reduce the temporal redundancies of the input videos. Variety of block search pattern is developed in the literature to match the blocks with reduced computation complexity but without affecting the visual quality. In this paper, we have integrated the square and hexagon search pattern with adaptive order to... Block motion estimation and compensation are played a major role in the video compression to reduce the temporal redundancies of the input videos. Variety of block search pattern is developed in the literature to match the blocks with reduced computation complexity but without affecting the visual quality. In this paper, we have integrated the square and hexagon search pattern with adaptive order to find the block motion estimation. This proposed search patterns is developed as AOSH search algorithm to find the best matching block without much considering large number of search points. Also, the searching function is formulated as trade-off criterion where, tangent weighted function is newly developed to evaluate the matching point. The proposed AOSH search algorithm and tangent weighted trade-off criterion is effectively applied to block estimation process with the aim of improving the visual quality and compressive performance. The objective of video compression is validated for the proposed method using three videos namely, football, garden and tennis. The quantitative performance of the proposed method and existing methods are analyzed using SSIM and CIR. The results prove that the proposed method obtained the good visual quality and compressive performance than the previous methods. Read More Read Less