Content-Based Image Retrieval Using Energy-Based Frequency Domain Features

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier.KeywordsLBPLocal binary patternLRBPLocal roughness binary patternSVMSupport vector machineKNN K-nearest neighbors

This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector.

Digital images can be easily manipulated due to availability of powerful image processing software. Passive authentication as a common challenge method of digital image authentication is extensively used to detect the copy-move forgery images. In this paper, a passive authentication scheme is proposed to authenticate copy-move forgery based on discrete cosine transform (DCT). At the feature extraction step, DCT is applied to image blocks and makes use of the means of DCT coefficients to represent image blocks. The size of feature vectors are optimized. At the matching step, a set number of packages is used to store the feature vectors. The similar blocks can be found by comparing the feature vectors that are contained in adjacent packages. The experimental results demonstrate that the proposed scheme can locate irregular and meaningful tampered regions and multiply duplicated regions. In addition, it can also locate the duplicated regions in digital images that are distorted by adding white Gaussian noise, Gaussian blurring and their mixed operations.

Traditionally, discrete cosine transform (DCT) features and Cartesian calibration are mainly utilized in joint picture expert group (JPEG) steganalysis. As well known, the steganalyzer without any modification can also work for spatial steganography. However, since JPEG and spatial embedding have different influences on DCT coefficients, this direct generalization is not quite reasonable. This essay studies the statistical moments and histogram variances of DCT coefficients under pixel domain modification. A novel steganalyzer for detecting spatial steganography is established by all alternating current (AC) coefficient features and Cartesian calibration. The experimental results state that the proposed steganalyzer outperforms the old DCT feature based method, as well as several spatial and wavelet feature based methods.

This paper extends an earlier work on designing a speech recognition system based on Hidden Markov Model (HMM) classification technique of using visual modality in addition to audio modality[1]. Improved off traditional HMM-based Automatic Speech Recognition (ASR) accuracy is achieved by implementing a technique using either RNN-based or CNN-based approach. This research is intending to deliver two contributions: The first contribution is the methodology of choosing the visual features by comparing different visual features extraction methods like Discrete Cosine Transform (DCT), blocked DCT, and Histograms of Oriented Gradients with Local Binary Patterns (HOG+LBP), and applying different dimension reduction techniques like Principal Component Analysis (PCA), auto-encoder, Linear Discriminant Analysis (LDA), t-distributed Stochastic Neighbor Embedding (t-SNE) to find the most effective features vector size. Then the obtained visual features are early integrated with the audio features obtained by using Mel Frequency Cepstral Coefficients (MFCCs) and feed the combined audio-visual feature vector to the classification process. The second contribution of this research is the methodology of developing the classification process using deep learning by comparing different Deep Neural Network (DNN) architectures like Bidirectional Long-Short Term Memory (BiLSTM) and Convolution Neural Network (CNN) with the traditional HMM. The proposed model is evaluated on two multi-speakers AV-ASR datasets named AVletters and GRID with different SNR. The model performs speaker-independent experiments in AVlettter dataset and speaker-dependent in GRID dataset.

Histogram refinement for texture descriptor based image retrieval

Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval

Palm print scanning is a widespread method for biometric identity detection which has some advantages over other methods including its simplicity and relatively lower cost. In this study, a novel methods for biometric verification and identification by contactless palm scanning technique is proposed. In the study, Ripplet-I Transform (R-IT) which is a generalized form of Curvelet Transform (CuT), have been used in addition to multi-resolution transforms which were previously used in the literature as palm print verification and identification methods such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Contourlet Transform (CoT). In addition, Principal Component Analysis (PCA) and Local Binary Pattern (LBP) have been utilized to increase the algorithm diversity. In order to investigate the effect of classification methods on the study results and the processing times, Artificial Neural Network (ANN), Euclidean Distance (ED) and Support Vector Machine (SVM) have been used separately for matching in the verification part of study. The performance of Convolutional Neural Network (CNN) as a classifier has also been examined. Verification and identification algorithms proposed in the study have been tested using palm print images of Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database (Version 1.0). The studies, that were carried out under two main sections yielded interesting results. At the end of the study, AUC (Area Under the ROC Curve) values ranging from 0.550 (Equal Error Rate (EER)= 0.4594) to 0.9875 (EER= 0.0336) were obtained for palm print verification. The highest AUC value without using LBP was obtained as 0.9563 (EER= 0.1096) using R-IT/CuT+DCT+CNN. Study results were showed that CNN is more successful than other classifiers without using LBP. It also has pointed out that the R-IT/CuT provides better results than the DWT and CoT. Using LBP in algorithms has increased success for ED, SVM and ANN. However, it has reduced overall for CNN. The highest AUC value (0.9875 and EER= 0.0336) was provided by the LBP+DWT+ED algorithm for palm print verification. The highest Identification Rate (IR) was achieved by using the LBP+CoT+ED algorithm with 84.444% for for palm print identification.

In the fingerprint recognition application utilizing more information other than minutiae is much helpful. We present here a fingerprint matching scheme based on transform features and their comparison. The technique described here obviates the need for extracting minutiae points to match fingerprint images. The proposed scheme uses Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) to create feature vector for fingerprints. After finding out the core point, fingerprint image of size 64times64 is cropped around the core point. The transform is applied on the cropped image without any pre-processing. The transform coefficients are arranged in specific manner and are used to obtain the feature vector in terms of standard deviation. The fingerprint matching is based on the minimum Euclidean distance between two feature vectors. Here database is formed by capturing 8 images per person using 500 dpi optical scanner. Training images used to form feature vector are 2, 4 or 6 per person. In the matching phase either all or remaining images are checked in identification mode to find out the percentage recognition rate. Comparison for all the three transform is presented here and it is observed that DCT and DFT gives better result as compared DWT.

The performance and effectiveness of CBIR scheme are directly associated with construction of small dimensional as well as salient image features respectively. So, in this paper, we have carried out the image retrieval process with small dimensional salient Image components or features as compare to the original image size and the imaze retrieval accuracy has been improved due to the consideration of local information rather than global information of Image data. Initially, the Image data is exploited in block level by discrete cosine transformation (DCT) and subsequently, some significant DCT coefficients are selected from each block as salient image components. However, all the DCT coefficients are not equally important in terms of visual perception and their dimension is also not negligible even in Image retrieval process. Later, selected AC coefficients are divided into four different groups. Later, some statistical parameters are computed from each group. Each statistical value is placed at a particular matrix. So, from each group, the number of matrices constructed is equal to the number of statistical parameters evaluated and one more matrix for DC coefficients is considered. Further, for construction of small dimensional feature vectors, gray level co-occurrence matrix (GLCM) is employed on all constructed matrices to derive the feature vector for a color component. The same procedure is employed on all three components and all feature vectors are combined toaether to form the final feature vector. The proposed CBIR structure is tested on three standard Image database i.e. Corel-lK, GHIM-10K and Olivia database and the experimental results demonstrate satisfactorily image retrieval and performance outperform other state-of-art schemes in many instances with respect to their precision values.

Purpose: Emerging technologies such as dedicated PET/MRI and MR-therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo-CTs. Methods: A single acquisition, 190-second UTE-mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water-filled tubes were placed in the FOV for trajectory-delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two-point Dixon water/fat separation and R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S-1) and relative spatial coordinates (S-2), were incorporated. A direct-mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo-CT. CT images served as the training data and, using a leave-one-out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo-CT declined dramatically when the spatial features, i.e. S-1 and S-2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo-CT were smaller when both the S-1 and S-2 were used together than when either the S-1 or the S-2 was used. Pseudo-CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct-mapping ANN approach is a technically accurate, clinically practical method for pseudo-CT generation and can potentially help improve the accuracy of MR-AC and MR-RTP applications. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees

This paper represents automatic facial expression analysis method named Local Directional Stigma Mean Patterns (LDSMP) for automatic facial expression analysis and image retrieval using content based facial expression image retrieval and CNN. The traditional local patterns such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) are applied for face recognition and expression analysis, calculated using relationship between the center pixel and neighboring pixels. The proposed method calculates the eight directional difference values then divided into the three ranges based on threshold values. Thus, the values are substituted with basic three positive values (+3, +2, +1) and three negative values (-3, -2, -1) to get more sensitive information from an image rather than aforementioned methods. The threshold can be select either static which is selected by user or dynamic is evaluated from image itself and supports to improve the efficiency. The performance of the proposed method is further improved by giving this patterns as input to the Convolutional Neural Networks (CNN) and compared with the existing methods LBP, LTP, and Directional Binary Code (DBC) in terms of Average Precision (AP), Average Recall (AR), and Average Retrieval Rate (ARR) using standard databases COREL 10K (DB1) and JAFFE (The Japanese Female Facial Expression) (DB2) and Extended Cohn-Kanade (CK +) (DB3) dataset.

In this paper, an integrated method of Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) is used for face recognition under different variations with illumination, pose and facial expression. LBP is applied as a preprocessing step to generate the LBP image which is then analyzed on a block-by-block basis. Facial features are extracted from each block using the DCT algorithm. The objective is to explore the performance of LBP under different illumination, pose and expression variations. Face images in the Yale database and ORL database are used in our experiments. A comparison on the sensitivities of the LBP operator in illumination, pose and expression variations is provided.

In this paper, a new method for off-line signature verification is proposed based on discrete cosine transform (DCT). The proposed approach has two stages, namely feature extraction and representation of signature using DCT followed by classification through support vector machine (SVM). The training signature samples are subjected to preprocessing to obtain binarized image, and DCT is employed on the binarized image. The upper-left corner block of size m X n is chosen as a representative feature vector for each trained signature sample. These small feature vector blocks are fed as an input to the SVM for training purpose. The SVM is used as a verification tool and trained with different number of training samples including genuine, skilled, and random forgeries. The proposed approach produces excellent results on the standard signature databases, namely CEDAR, GPDS-160, and MUKOS—a Kannada signature database. In order to demonstrate the superiority of the proposed approach, comparative analysis is provided with many of the standard approaches.KeywordsDiscrete cosine transformSupport vector machineSignature verification

One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image’s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%.