When the usages of electronic mail continue, unsolicited bulk email also continues to grow. These unsolicited bulk emails occupies server storage space and consumes large amount of network bandwidth. To overcome this serious problem, Anti-spam filters become a common component of internet security. Recently, Image spamming is a new kind of method of email spamming in which the text is embedded in image or picture files. Identifying and preventing spam is one of the top challenges in the internet world. Many approaches for identifying image spam have been established in literature. The artificial neural network is an effective classification method for solving feature extraction problems. In this paper we present an experimental system for the classification of image spam by considering statistical image feature histogram and mean value of an block of image. A comparative study of image classification based on color histogram and mean value is presented in this paper. The experimental result shows the performance of the proposed system and it achieves best results with minimum false positive.
Abstract-Dynamicbackground subtraction in noisy environment for detecting object is a challenging process in computer vision. The proposed algorithm has been used to identify moving objects from the sequence of video frames which contains dynamically changing backgrounds in the noisy atmosphere. There are many challenges in achieving a robust background subtraction algorithm in the external noisy environment. In connection with our previous work, in this paper, we have proposed a methodology to perform background subtraction from moving vehicles in traffic video sequences that combines statistical assumptions of moving objects using the previous frames in the dynamically varying noisy situation. Background image is frequently updated in order to achieve reliability of the motion detection. For that, a binary moving objects hypothesis mask is constructed to classify any group of lattices as being from a moving object based on the optimal threshold. Then, the new incoming information is integrated into the current background image using a Kalman filter. In order to improve the performance, a post-processing has been done. It has been accomplished by shadow and noise removal algorithms operating at the lattice which identifies object-level elements. The results of post-processing can be used to detect object more efficiently. Experimental results and analysis show the prominence of the proposed approach which has achieved an average of 94% accuracy in real-time acquired images.
Identification of an object from a dynamic background is a challenging process in computer vision and pattern matching research. The proposed algorithm identifies moving objects from the sequence of video frames which contains dynamically changing backgrounds in the noisy environment. In connection with our previous work, here we have proposed a methodology to perform skeletanization of an object and identifies it. The recent vehicle recognition methods could fail to recognize an object and produce more false acceptance rate(FAR) or false rejection rate (FRR). This paper recommends a method for object identification using weighted distance to extract features. Experimental results and comparisons using real data demonstrate the pre-eminence of the proposed approach.
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