Binary image matching using scale invariant feature and hough transforms

The Scale Invariant Feature Transform (SIFT) feature can be used for an image distance measure. SIFT keypoints are mainly located on high-contrast regions of an image, such as object edges and textures, which are often visually important. Therefore, it can be said that if a resized image keeps SIFT features which the corresponding original image has, the resized image is visually acceptable. To the best of our knowledge, there is no resizing method for explicit preservation of SIFT features. In this paper, we propose a resizing method based on mesh warping for explicit preservation of SIFT features. The effectiveness of our resizing method is demonstrated by experiments.

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

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.

Scale Invariant Feature Transform (SIFT) since its first apparition in 2004 has been (and still is) extensively used in computer vision to classify and match objects in RGB and grey level images and videos. However, since the descriptor used in SIFT approach is based on gradient magnitude and orientation, it has always been considered as texture feature and received less interest when treating binary images. In this work we investigate the power of discrimination of SIFT applied to binary images. A theoretical and experimental studies show that SIFT can still describe shapes and can be used to distinguish objects of several classes.

In this paper, a novel face representation in terms of dense local image descriptors is proposed. Scale Invariant Feature Transform (SIFT) and Gabor, two of the most popular local image descriptors, at dense grid pixels of a face image are used to represent the face. The efficiency of the representation has been investigated in gender recognition. There are four problems when applying the SIFT to facial gender recognition. (1) There may be only a few keypoints that can be found in a face image due to the missing texture and ill-illumined faces; (2) The SIFT descriptors at the keypoints (we called it sparse SIFT) are distinctive whereas alternative descriptors at non-keypoints (e.g. grid) could cause negative impact on the accuracy; (3) Most of the existing methods employ SIFT descriptors matching in which relative larger image size is required in order that enough keypoints can be found to support the matching and (4) The matching is assumed that the faces are well registered. We provide solutions to the above difficulties in this paper and the problem of recognizing gender using the combination of SIFT descriptors and Gabor of face images is studied. The Gabor representations of the face images are fused with the dense SIFT at the feature-level to improve the accuracy. AdaBoost is adopted to select features and form a strong classifier. The experimental results on a large set of faces have shown that the proposed method can achieve high accuracies even for faces that are not aligned.

In recent years, Ambient Intelligence (AmI) has attracted increased attention within the advanced technology industry in an effort to modernize and develop a more intelligent and reliable information system. Technologies to detect a specific object in images are expected to further expand to wide range of applications, such as car detection functions for Intelligent Transport System (ITS) and other systems. Computer vision and pattern recognition are emerging fast and will continue to grow together with local feature detection methods. In this paper, we propose an object detection and tracking system which is based on Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF) feature extraction methods. From the evaluation results, we observe that the accuracy of matched keypoints of SURF algorithm are higher than SIFT.

Efficient detection and reliable matching of image features constitute a fundamental task in computer vision. When real-time operation is required, the solution to this problem becomes a real challenge, because of increased processing requirements. Scale Invariant Feature Transform (SIFT) is considered as a stable and robust algorithm for the extraction of invariant features, however special hardware is required for high frame-rate applications, in order to overcome its computational complexity in real-time. In this paper, a complete processor for SIFT feature matching in video sequences is presented. It performs SIFT feature extraction and matching, as well as rejection of false correspondences using the random sample consensus (RANSAC) algorithm. The processor was evaluated using metrics like response, repeatability and recall. It was found that the proposed fixed-point hardware implementation has a comparable performance with software implementations of SIFT. The processor architecture can process more than 36 fps with resolution 640×480, when it is implemented in Cyclone IV FPGA technology.

Scale-Invariant Feature Transform (SIFT) is one of the most popular local image features that are widely used in computer vision, image processing and image retrieval. In this paper we study the relation between the SIFT descriptor and its matching accuracy. We propose a method to quantitatively assess the quality of a SIFT feature descriptor in terms of robustness and discriminability. This would enable us to gain a better understanding of the strength and limitations of SIFT in emerging applications of SIFT-based image hash, and also to improve matching accuracy and efficiency in applications such as object search. The experimental results demonstrate the effectiveness of the proposed method.

Image category recognition is important to access visual information on the level of objects and scene types. This paper presents an automatic recognition system of scene and object with PCA-SICEF feature for digital color images. SICEF (scale-invariant color and edge feature) is an extension of the conventional local SIFT (scale-invariant feature transform) feature, which only include edge invariance of local image region but not any color information. So the SIFT feature is not enough for distinguish image categorization especially for scene types, where the color information plays an important role for recognition. Therefore, we improve SIFT by including color feature for local image region, and name it as SICEF feature. However, the dimension of the extracted SICEF feature is so high that we use PCA (principle component analysis) to reduce the dimension, and then, use the PCA-domain SICEF (PCA-SICEF) for image classification. Experimental results show that it is much more efficient by our proposed PCA-SICEF feature than conventional SIFT feature.

In allusion to non-rigid registration of medical images, the paper gives a novel algorithm based on improved Scale Invariant Features Transform (SIFT) feature matching algorithm. First, Harris corner detection algorithm is used in the process of scale invariant feature extraction, so the number of right matching points is increased; with regard to the feature points detected in the scale space, an improved SIFT feature extraction algorithm with global context vector is presented to solve the problem that SIFT descriptors result in a lot of mismatches when an image has many similar regions. On this basis, affine transformation is chosen to implement the non-rigid registration, and weighted mutual information (WMI) measure and Particle Swarm Optimization (PSO) algorithm are also chosen to optimize the registration process. The experimental results show that the method can achieve better registration results than the method based on mutual information.

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.

Experiments on iris recognition using Scale Invariant Feature Transform (SIFT) features and several distance measures we defined are presented. Two well-known iris databases, UBIRIS and UPOL, were used as primary data. From unsegmented images of UPOL database we derived other two test datasets: one using manually segmented images and another one using automatic segmented images. The distance measures defined using SIFT feature were applied for iris recognition using as input the four datasets: UBIRIS, UPOL and those two derived from UPOL. The experiments results revealed that the tested methods can be used as a simple, fast and very efficient filter, whose output can be then processed by more sophisticated methods.

In this paper we propose a novel feature based on SIFT (Scale Invariant Feature Transform) algorithm1 for the robust representation of local visual contents. SIFT features have raised much interest for their power of description of visual content characterizing punctual information against variation of luminance and change of viewpoint and they are very useful to capture local information. For a single image hundreds of keypoints are found and they are particularly suitable for tasks dealing with image registration or image matching. In this work we stretched the spatial coverage of descriptors creating a novel feature as composition of keypoints present in an image region while maintaining the invariance properties of SIFT descriptors. The number of descriptors is reduced, limiting the computational weight, and at the same time a more abstract descriptor is achieved. The new feature is therefore suitable at describing objects and characteristic image regions. We tested the retrieval performance with a dataset used to test PCA SIFT2 and image matching capability among images processed with affine transformations. Experimental results are reported.

Indexing images represents a necessary tool for various domains related to computer vision such as video surveillance and movements analysis. Visual characteristics of image such as color, texture, shape are used to identify the content of these frames. The high increase of data (Big Data) makes us face a problem of curse of dimensionality. In this paper, we propose a cloud-based technique for images indexing images using PCA (Principal component analysis) and a binary tree. Our work consists of exploiting SIFT (Scale Invariant Feature Transform) and SURF (Speeded up Robust Features) descriptors as frames features, PCA as dimensionality reduction method of SIFT and SURF features and the binary tree representation of these images in order to accelerate the research phase and reduce the storage space. Our approach consists of developing a system based on three phases: indexing images with SIFT and SURF features, compression within the application of PCA and finally the image retrieval in the cloud.

Face recognition includes analysis of an image and extracting its facial features which will help to discriminate it from others. Scale invariant feature transform (SIFT) to extract distinctive invariant features from images can be used to perform reliable matching. The features extracted are invariant to rotation, image scale and illumination. Systematic investigation of face recognition using SIFT features has been done. Being high distinctive features, every feature can be matched correctly with high probability against a huge database of features from many images. Result shows that SIFT is flexible recognition algorithm as compared to Contour matching algorithm for heterogeneous images. Both the algorithms are experimentally evaluated on AT&T, YALE and IIT-KANPUR databases with moderate subject size. Though Contour matching provides computational simplicity, SIFT provides efficient face recognition technique under pose, expression and varying illumination condition. Experimentally it confirms that Contour matching outperforms when the database is small but for large databases Scale Invariant Feature Transform (SIFT) gives more than 90% recognition rate.