Efficient large scale near-duplicate video detection base on spark

Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform (SIFT) algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval (CBIR) applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis (LDA) is used to transform the local features to global features while reducing dimensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.

In the object recognition based on the bag of visual words, we focus on several main feature extraction algorithms. The Scale Invariant Feature Transform (SIFT) that based on local features with good significance and robustness has became a popular feature extraction method. But we take into consideration that the SIFT will generate a large number of high-dimensional feature vectors, which will increase the computational cost. In this paper, a novel feature extraction algorithm by means of SIFT to fuse region information entropy (SIFT-entropy) is proposed. The algorithm can improve the quality of the feature extracted from the image by cluster similar key points to removing the noisy key points. So a more discriminative high quality “visual word” codebook could be generated. We made a comprehensive comparison between the proposed method and the original SIFT method on Caltech-101 database. The experimental results show that this improvement can reduce the dimension of the feature space, and has higher classification accuracy.

We investigated the application of multiresolution global and local texture features to reduce false-positive detection in a computerized mass detection program. One hundred and sixty-eight digitized mammograms were randomly and equally divided into training and test groups. From these mammograms, two datasets were formed. The first dataset (manual) contained four regions of interest (ROIs) selected manually from each of the mammograms. One of the four ROIs contained a biopsy-proven mass and the other three contained normal parenchyma, including dense, mixed dense/fatty, and fatty tissues. The second dataset (hybrid) contained the manually extracted mass ROIs, along with normal tissue ROIs extracted by an automated Density-Weighted Contrast Enhancement (DWCE) algorithm as false-positive detections. A wavelet transform was used to decompose an ROI into several scales. Global texture features were derived from the low-pass coefficients in the wavelet transformed images. Local texture features were calculated from the suspicious object and the peripheral subregions. Linear discriminant models using effective features selected from the global, local, or combined feature spaces were established to maximize the separation between masses and normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classifier performance. The classification accuracy using global features were comparable to that using local features. With both global and local features, the average area, Az, under the test ROC curve, reached 0.92 for the manual dataset and 0.96 for the hybrid dataset, demonstrating statistically significant improvement over those obtained with global or local features alone. The results indicated the effectiveness of the combined global and local features in the classification of masses and normal tissue for false-positive reduction.

In this study, we investigated how shapes are classified based on local and global features by four representative convolutional neural networks (CNNs), i.e., AlexNet, VGG, ResNet and Inception. While the local features are based on simple components, such as orientation of line segment, the global features are based on the whole object, such as whether an object has a hole. For example, solid triangles and solid squares are differentiated by local features, solid circles and rings are differentiated by a global feature. Two sets of experiments were performed in this research. In the first experiment, we examined how the four CNNs pre-trained on ImageNet (with transfer learning) learned to differentiate the regular shapes (equilateral-triangles, squares, circles and rings). Our results showed that the pre-trained CNNs exhibited faster learning rates in the tasks discriminating the local features than in the tasks discriminating the global feature. However, the transfer learning of discriminating the global feature in regular shapes were better generalized to irregular shapes than the transfer learning of discriminating local features. In the second experiment, the CNNs were trained from scratch (with random weights initialization) to discriminate local and global features in regular and irregular shapes. Different from the transfer learning, the CNNs exhibited faster learning rates in discriminating the global feature than the local features. Similar to transfer learning, the CNNs exhibited excellent learning generalization to discriminating the global feature of irregular shapes, but poor learning generalization to discriminating the local features in the irregular shapes. The overarching goal of this research is to create a paradigm and benchmark to directly compare how the CNNs and primate visual systems process geometrical invariants. In contrast to the ImageNet approach which employs natural images to train CNNs, we employed the “ShapeNet” approach which features geometrical shapes with well-defined properties. The ShapeNet approach will not only help elucidate the strengths and limitations of CNN computation, but also provide insights into visual information processing in the primates.

Content based image retrieval systems (CBIR) are used to search images on the basis of their visual content in a huge image database. This approach uses a multi-clustering technique with a multi-searching process. In addition it integrate, Mean SIFT (Scale Invariant Feature Transform) descriptor as local feature and HSV(hue, saturation, value) histogram as global feature. Our proposition aims to a maximum separation of the execution to gain time and keep the performance of each descriptor. Local and global features are combined for more relevant results. Getting several views to relevant results to cover the subjectivity in displaying results. This article, detailed our proposed method with a comparison with the FIRE (Flexible Image Retrieval) Engine and LIRE (Lucene Image Retrieval). The results demonstrate the feasibility and relevance of our proposition.

With the advent of e-commerce, digital services and social media, scammers have changed their way to gain illegal benefits in various forms such as capturing the credit card information or exploiting personal cloud accounts which is termed as phishing. For this reason, against this cyber crime, last two decades have witnessed a variety of combatting methodologies like HTML content based similarity analysis, URL based classification and recently visual similarity based matching since phishing web pages visually mimic to their legitimate counterparts in order to create an illusion to deceive innocent users. To this end, in this study, we propose a computer vision and machine learning based approach in order to classify whether a suspicious web page is phishing and further recognize its original brand name. In this regard, we have utilized and investigated two different local image descriptors namely Scale Invariant Feature Transform (SIFT) and DAISY. Apart from their common properties such as scale invariance, the aforementioned descriptors have apparent differences such that in addition to rotational invariance, SIFT employs key-point based sampling whereas DAISY applies dense sampling by default. Therefore, we first aimed to investigate the feasibility of these two local image descriptors in addition to revealing the effects of sampling strategy and rotational invariance in problem domain. Furthermore, in order to create a discriminative representation of a web page, we followed the bag of visual words (BOVW) approach having different vocabulary sizes such as 50, 100, 200 and 400. In order to evaluate the proposed approach, we have utilized a publicly available phishing dataset including snapshots of webpages sampled from both 14 different highly phished brands and ordinary legitimate web pages yielding a challenging open-set problem. The aforementioned dataset involves 1313 training and 1539 testing image samples in total. The visual features extracted via SIFT and DAISY were first transformed to a BOVW histogram and fed to three different machine learning methods such as SVM, Random Forest and XGBoost. According to the conducted experiments, based on a 400-D visual vocabulary, SIFT descriptor along with XGBoost has been found as the best descriptor-learner configuration having reached up to 89.34% validation accuracy with 0.76% false positive rate. Moreover, SIFT has outperformed DAISY descriptor in all settings. As a result, it has been shown that SIFT descriptors equipped with BOVW representation can be effectively used for brand identification of phishing web pages.

This study analyzes the effectiveness of the global (the whole face) and local (regions of eyes, nose, and mouth) features for face recognition. Features describing human faces are encoded in local ternary patterns. The two-class support vector machine is used as the supervised learning algorithm for training recognition models. In the recognition process, recognition modes based on the global features and local features are cascaded. For identifying a face image, the local features are used iteratively for filtering out candidates that can not be clearly identified by the global features, until the one with highest possibility is concluded. The experimental results show that cascading the recognition models of global and local features obtains better classification accuracy than the single classification process.

Sentence similarity is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. However, a variety of linguistic expressions and ambiguities of words in sentences make it difficult to measure sentence similarity. Many studies show that using local features or global features of a sentence will produce satisfactory sentence representations that can be utilized to measure sentence similarity. Local features reflect the relationships of adjacent words for each sentence and the sequence information of a sentence are usually expressed by global features. However, local features lack abilities to capture sequence information while a small amount of extracted global features is not enough to produce sentence representations with good qualities. In this paper, we propose A Hybrid Model combining Local and Global features into a Siamese Network (HM-LGSN) for sentence similarity calculation. We first propose a new convolution neural network architecture called group convolution neural network to extract the most representative local features (or word semantic features). Then we combine these new features with pre-trained embeddings of words as input to the Bidirectional Gated Recurrent Units to extract global features of sentences. Finally, we select the global features to form sentence representations and calculate sentence similarity through Manhattan distance. The experimental results on SICK, MSRVID, STS-B datasets show that the accuracy of our proposed model is significantly improved by combining local features and global features.

SIFT (scale invariant feature transform) is one of the most robust and the widely used image matching algorithms based on local features. It has been used in the aspect of the cameras target matching. For its high complexity, this paper proposes a target matching method for multiple cameras by combining SIFT and the rotation-invariant LBP (local binary patterns). The object detection is obtained by improved Back-ground subtraction technique, and particle filter is used for region-based object tracking. The descriptor combining SIFT and the rotation-invariant LBP is calculated for every object region. The object matching is obtained by comparing the distance of feature matrix. Experimental results show that the proposed algorithm is more rapid than the standard SIFT algorithm while the performance is favorably compared to the standard SIFT algorithm, and it is robust in various scenes.

Image is an important class of matrix structured data. In practical application scenarios, local features of data often contain more discriminative information. Considering that the existing sparse feature selection algorithms rarely consider the local structure information contained in image data, this paper proposes a sparse feature selection algorithm for image data based on local feature based sparse linear discriminant analysis (LFSLDA). Firstly, the scale-invariant feature transform (SIFT) local feature descriptor matrix is used to represent the original image matrix data, and then row and column transformation are carried out respectively. In order to better select the features with more discriminant information and no correlation between each other, the algorithm introduces the generalized uncorrelation constraint and applies it to the total divergence matrix, which not only avoids the singularity problem, but also preserves the structure of the data. Experimental results show that this algorithm can make better use of the local feature information of matrix data and have better performance than other related algorithms on six image data sets.

This paper deals with the retrieval of scene-of-cri me (or scene) shoeprint images from a reference database of shoeprint images by using a n ew local feature detector and an improved local feature descriptor. Our approach is based on novel modifications and improvements of a few recent techniques in this area: (1) the scale a dapted Harris detector, which is an extension to multi-scale domains of the Harris corner detector; (2) automatic scale selection by the characteristic scale of a local structure. (3) SIFT (Scale-Invariant Feature Transform), one of the most widely investigated descriptors in recent year s. Like most of other local feature representations, the proposed approach can also be divided into two stages: (i) a set of distinctive local features are selected by first de tecting scale adaptive Harris corners where each of them is associated with a scale factor, and then selecting as the final result only those corners whose scale matches the scale of blob-like structur es around them. Here, the scale of a blob-like structure is detected by the Laplace-based scale se lection. (ii). for each feature detected, an enhanced SIFT descriptor is computed to represent t his feature. Our improvements lead two novel methods which we call the Modified Harris-Laplace (MHL) detector, and the enhanced SIFT descriptor. In this paper, we demonstrate the application of t he proposed scheme to the shoeprint image retrieval problem using six sets of synthetic scene images, 50 images for each, and a database of 500 reference shoeprint images. The ret rieval performance of the proposed approach is significantly better, in terms of cumul ative matching score, than the existing methods used in this application area, such as edge directional histogram, power spectral distribution, and pattern & topological spectra.

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which combines both global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from some spatially partitioned image patches by Gabor wavelet transform. After this, multiple classifiers are obtained by applying Fisher Discriminant Analysis on global Fourier features and local patches of Gabor features. All these classifiers are combined to form a hierarchical ensemble by sum rule. We evaluated the proposed method using Face Recognition Grand Challenge (FRGC) experimental protocols and database known as the largest data sets available. Experimental results on FRGC version 2.0 data set have shown that the proposed method achieves a verification rate of 86%, while the best reported was 76%.

This mixed methods study sought to extend what is known about preservice teachers’ [PSTs’] enactment of the core practice of reading and responding to students’ writing by examining their reading of elementary students’ writing at the start and end of writing-focused methods coursework. PSTs’ reading of students’ writing involves analysis of children’s writing strengths and needs and undergirds their teaching of writing, influencing their feedback and instructional decision-making. Participants included 115 elementary PSTs. Their identifications of local and global text features on a beginning- and end-of-semester assessment task were examined. Overall, PSTs showcased differences in their reading of children’s writing at the two time points, displaying a lessened focus on local features (e.g., spelling, punctuation) and a greater focus on global features (e.g., content, structure) at the end of the semester. This trend was especially apparent in their identification of children’s instructional needs. Moreover, PSTs’ reading of children’s informative and persuasive writing tended to be similar to their reading of children’s narrative writing at both time points. The results offer insight into the text features PSTs make available for use in their subsequent responding and suggest the importance of highlighting global text features among children’s instructional needs.

The huge amount of redundant multimedia data, like video, has become a problem in terms of both space and copyright. Usually, the methods for identifying near-duplicate videos are neither adequate nor scalable to find pairs of similar videos. Similarity self-join operation could be an alternative to solve this problem in which all similar pairs of elements from a video dataset are retrieved. Nonetheless, methods for similarity self-join have poor performance when applied to high-dimensional data. In this work, we propose a new approximate method to compute similarity self-join in sub-quadratic time in order to solve the near-duplicate video detection problem. Our strategy is based on clustering techniques to find out groups of videos which are similar to each other.

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.