Content-based authentication algorithm for binary images

This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes and mouth are extracted. In the classification of expression, using MCE criteria is adopted to improve the performance of HMM. The results show that the performance of expression recognition system can be improved to some extent by using Zernike moments and MCE criterion.

This paper presents an intelligent texture anti-counterfeiting method based on human visual characteristics and DFT. By using a part of sign sequence of DFT coefficients as the feature vector of texture images, we extract a visual feature vector and then create a database of texture images' feature vectors. After comparing the Normalized Cross-correlation (NC) between the feature vectors, we achieve the purpose of automatic identification. The experimental results show that the proposed algorithm has strong robustness against common and geometric attacks.

The paper describes a new method to extract and cluster image features for effective still image databases. The feature vectors concerning color and texture are extracted using the multiresolution wavelet. In contrast to traditional image databases where feature vectors extracted from stored images are stored and used to match the feature vector of the input image for similarity retrieval, we use the self-organizing map neural network for clustering stored images. No feature vectors are stored in the databases, which saves storage space. A prototype image database is developed and some experiments are performed using it. The paper reports on the architecture and experimental results.

In this paper, an automatic age simulation method used for robust face recognition is proposed. We first use a shape and texture vectors to represent a facial image by projecting it in the eigenspace of shape or texture. Then we use age function combined with aging way classification to estimate age. And we use estimated age, typical vector creating function and the feature vector of the original test image to generate the synthesized feature vectors at target age. At last we reconstruct the shape and texture in eigenspaces and combine them to synthesize facial image at target age. Experiments show that the proposed method can effectively "change" the age of face images, and help to get face recognition result robust to age variation

In content based image retrieval (CBIR) process, every image has been represented in a compact set of local visual features i.e. color, texture, and/or shape of images. This set of local visual features is known as feature vector. In the CBIR process, feature vectors of images have been used to represent or to identify similar images in adequate way. As a result, feature vector construction has always been considered as an important issue since it must reflect proper image semantics using minimal amount of data. The proposed CBIR scheme is based on the combination of color and texture features. In this work initially, we have converted the given RGB image into HSV color image. Subsequently, we have considered H (hue), S (saturation), and V (intensity) components for extraction of visual image features. The texture features have been extracted from the V component of the image using 2D dual-tree complex wavelet transform (2D DT-CWT) where it analyzes the textural patterns in six different directions i.e. ±15\(^{\circ }\), ±45\(^{\circ }\), and ±75\(^{\circ }\). At the same time, we have computed the probability histograms of H and S components of the image respectively and subsequently those are divided into non-uniform bins based on cumulative probability for extraction of color based features. So, in this work both the color and texture features have been extracted simultaneously. Finally, the obtained features have been concatenated to attain the final feature vector and same is considered in image retrieval process. We have tested the novelty and performance of the proposed work in two Corel, two objects, and, a texture image datasets. The experimental results reveal the acceptable retrieval performances for different types of datasets.

In the CBIR- (Content Based Image Retrieval) technique requires low-level or primitive features- color, texture, or other data that can be taken from its image Extracting feature vectors of database images as well as query image can be calculated with the help of slant transform by considering DC & 3 AC coefficients obtained in each block of an image. Slant transform represents the gradual brightness changes in an image line effectively. By calculating the difference between feature vector data base and feature vector for a query by using the distance measuring techniques. The vector of the smaller distance is the closest to query image. The experiment is performed in the Corel 500 Image Database. Finally, CBIR results are evaluated by the recall, precision, and F-Score.

Weed classification is a serious issue in the agricultural research. Weed classification is a necessity in identifying weed species for control. Many classification techniques have been used to identify weed based on images, however, most of the techniques only measure the percentages of accuracy but the detailed of classifier parameter are not analyzed and discussed. Therefore, in this work, feature vectors of weed images extracted using Gabor Wavelet and Fast Fourier Transform (FFT) were employed in analyzing weed pattern based on images using Support Vector Machines (SVM). The decision boundaries of the categorized extracted feature vectors are illustrated and optimal feature vectors are identified. Results are discussed and displayed with illustrations to prove the SVM classifier performance.

Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR, the features in a spatial domain, such as color, texture, shape, and point features, are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. In this paper, we are proposing two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image: DCT_256_Zigzag and DCT_256_2 × 2. These methods take into consideration the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For a query image, its feature vector is compared with feature vectors of every other image in the database using d1-distance, and the images with least distance are considered most similar images to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets Corel-1000, Corel-5000, Corel-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.KeywordsCBIRDCTFrequency domainInterchannel votingLBPTMRE

Though traditional classification methods show well performance in classification tasks, most of them mainly lay emphasis on ‘classification’ rather than ‘cognition’. When a new object that has never been seen is encountered, the traditional methods falsely default the image as a certain category that has been studied, however, humans can first identify the image as new. In this paper, we present a memory model for visual images classification based on residual neural network and Bayesian decision (VICRB). First, the feature vectors of the visual images for each category are extracted with residual neural network and each feature component may be correctly copied or randomly produced. Then the processes about how the visual images represent, store and retrieve are modeled. The feature vector of test images is matched with the feature vectors of learned images and the likelihood ratio is computed according to probabilistic inference theory. Finally, the odds value in favor of an old over a new image is computed by all likelihood values. According to the odd value, the Bayesian decision rule is applied to the image classification. Experimental results on two benchmark images datasets show that the presented memory model performs well in images classification tasks.

A new and efficient methodology for investigating and studying the problem of estimating the similarity between images is presented in this paper. The new methodology can be applied to the similarity measure of both the binary and gray-scale images. A morphological distance is used to measure the similarity between images. The shape representation for this new method utilizes morphological image opening and closing of the input image at different scales of a primary and rotated structuring elements. The areas of the opened and closed images are computed for each structure element at each scale to construct the feature vector of each input image. Because the operations involved in morphological processing relate directly to its content, it is compatible with human visual system which is insensitive to some changes on images such as rotation, noise, scale, and filtering also it proves to be more useful than convolution operations in industrial applications for detect identification.

With the development of 3D printing technology and lots of available 3D model digital files in the form of stl, 3ds, obj, etc., on the internet, there are many illegally distributed 3D model digital files which are protected by copyright law, and some are already printed. In this paper, we propose a method that matching shape between printed 3D model and digital 3D model based on 2D views and Zernike Moments. The printed 3D model is first photographed into several pictures by a camera, the digital 3D model is also captured into several rendering images by a virtual camera using a computer program. Next, both sides of the images are converted into binary images with denoising image processing. Then, Zernike Moments features are extracted from the binary images. Finally, we can yield similarity between the printed one and digital one by comparing the Zernike Moments.

Content based image retrieval (CBIR) is a technique in which images are indexed by extracting their low level features or high level features or both. This paper presents performance & comparative study of the result obtained by the two different approaches of CBIR system. The resultant retrieved images by the CBIR system is based on two approaches. Both approaches of the system using two major processes one as feature extraction and other as search and retrieval. The first approach is based on extracting the features using binary tree structure, color & texture and the second approach is based on extracting shape features using Canny Edge Detection. In both the approaches the feature vector of the query image is compared with the feature vector of the each image present in the database. In the first approach the images are retrieved and arranged according to their matching score or rank value and in the second approach retrieved images are arranged according to their mean and correlation values.

In this paper, we propose a novel thermal face recognition based on physiological information. The training phase includes preprocessing, feature extraction and classification. In the beginning, the human face can be depicted from the background of thermal image using the Bayesian framework and normalized to uniform size. A grid of 22 thermal points is extracted as a feature vector. These 22 extracted points are used to train Linear Support Vector Machine Classifier (linear SVC). The classifier calculates the support vectors and uses them to find the hyperplane for classification. A feature vector of testing image is inputted to the classifier for face recognition. Our contribution is that the proposed method firstly applies temperature information in face recognition. Experimental results prove the effectiveness of the proposed method.

A new scene classification method is proposed based on the combination of local Gabor features with a spatial pyramid matching model. First, new local Gabor feature descriptors are extracted from dense sampling patches of scene images. These local feature descriptors are embedded into a bag-of-visual-words (BOVW) model, which is combined with a spatial pyramid matching framework. The new local Gabor feature descriptors have sufficient discrimination abilities for dense regions of scene images. Then the efficient feature vectors of scene images can be obtained byK-means clustering method and visual word statistics. Second, in order to decrease classification time and improve accuracy, an improved kernel principal component analysis (KPCA) method is applied to reduce the dimensionality of pyramid histogram of visual words (PHOW). The principal components with the bigger interclass separability are retained in feature vectors, which are used for scene classification by the linear support vector machine (SVM) method. The proposed method is evaluated on three commonly used scene datasets. Experimental results demonstrate the effectiveness of the method.

In this study, a hybrid security system is proposed. The proposed system is composed of two subsystems namely iris recognition system (IRS) and speaker recognition system (SRS). Pre-processing, feature extraction and feature matching are the main steps of these systems. In IRS subsystem, Gaussian filter, Canny edge detector, Hough transform, and histogram equalization is performed for pre-processing, respectively. After that, by applying 4-level Discrete Wavelet Transform (DWT) to pure iris image, the iris image is decomposed into four sub-bands (LL4, LH4, HL4 and HH4). In order to extract the feature vector from iris pattern, the LH4, HL4 and HH4 sub-bands (matrices) are merged into one matrix. Finally the matrix is transformed in vector to obtain the feature vector of iris image. For SRS subsystem, the pre-processing step includes spectral arrangement, silence part removing and band limitation operations. After pre-processing, frame blocking and windowing are applied to the long-term speech samples and then Fast Fourier Transform (FFT) is performed for the each short-term speech segments (frames). Finally, the Mel Frequency Cepstral Coefficients (MFCC) technique is performed in order to obtain feature vector of the speech. The feature matching step of both IRS and SRS is implemented with Dynamic Time Warping (DTW) which is an efficient algorithm to measure the distance between two vectors. According to the DTW results, the false acceptance rate (FAR) is zero and false rejecting rate (FRR) is about 4 % for the proposed hybrid system.