Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

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.

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.

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.

There is a growing trend for using content‐based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will be done on. Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale‐invariant feature transform (SIFT), speeded‐up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature‐based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel‐1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%–3.6% across capabilities. These findings underscore the effectiveness of Hausdrauff distance in enhancing retrieval precision within the CBIR framework.

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.

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

In this paper, we present a random-forest based fast cascaded regression\nmodel for face alignment, via a novel local feature. Our proposed local\nlightweight feature, namely intimacy definition feature (IDF), is more\ndiscriminative than landmark pose-indexed feature, more efficient than\nhistogram of oriented gradients (HOG) feature and scale-invariant feature\ntransform (SIFT) feature, and more compact than the local binary feature (LBF).\nExperimental results show that our approach achieves state-of-the-art\nperformance when tested on the most challenging datasets. Compared with an\nLBF-based algorithm, our method can achieve about two times the speed-up and\nmore than 20% improvement, in terms of alignment accuracy measurement, and save\nan order of magnitude of memory requirement.\n

With the huge amount of web video data and its exponential growth in recent years, there are new challenges in Near-Duplicate Video Detection (NDVD) which have attracted much attention owing to its wide applications. One of the problems is how to extract discriminative features to achieve higher precision, and the other problem is how to improve the efficiency of large scale video analysis. Existing methods have predominantly focused on improving the precision and achieve some success on NDVD. However, it is an extremely challenging task to guarantee the higher precision and efficiency simultaneously. To balance the efficiency and precision, we propose a Multi-Feature based Parallel System (MFPS) for large scale NDVD to overcome these challenges. For feature extraction, we combine local and global features to accurately represent the critical information of video. In our work, not only Scale-Invariant Feature Transform (SIFT) but also Local Maximal Occurrence (LOMO) is introduced as local features and Color Name (CN) is adopted as global feature. Meanwhile, for SIFT and CN features, Bag-of-Visual-Words (BoVW) method is used to quantize the features due to its accuracy and efficiency in NDVD. Furthermore, we design a similarity measure for video pairs which is inspired by hough transform and sliding window idea. Finally, we implement the system on parallel cloud computing platform based on spark. A comprehensive evaluation is conducted on CC_WEB_VIDEO dataset which includes 12790 videos and 27% near-duplicates. Experimental results show that the proposed method is able to achieve some improvements in term of mAP against other methods. Moreover, our approach is efficient and achieves 5.8X speedup.

Due to the advancements in digital technologies and social networking, image collections are growing exponentially. The important aim in content-based image retrieval (CBIR) is to reduce the semantic gap issue that improves the performance of image retrieval. In this paper, the objective is achieved by introducing effective visual words fusion technique based on speeded-up robust features (SURF) and histograms of oriented gradients (HOG) feature descriptors. HOG is used to extract global features, whereas SURF is used for the extraction of local features. Global features are preferred for large-scale image retrieval, whereas local features perform better on those systems that support semantic queries with close visual appearance. Moreover, SURF is scale and rotation-invariant as compared to HOG descriptor and it works better for low illumination. On the contrary, HOG performs better for scene-recognition- or activity-recognition-based applications. In the proposed technique, visual words fusion of SURF and HOG feature descriptors is carried which performed better than features fusion of SURF and HOG feature descriptors as well as state-of-the-art CBIR techniques. The proposed technique based on visual words fusion gives classification accuracy of 98.40% using support vector machine while image retrieval accuracy of 80.61%. Qualitative and quantitative analyses performed on four standard image collections namely, Corel-1000, Corel-1500, Corel-5000, and Caltech-256 demonstrate the effectiveness of the proposed technique based on visual words fusion of SURF and HOG feature descriptors.

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.

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.

Road visual navigation relies on accurate road models. This study was aimed at proposing an improved scale-invariant feature transform (SIFT) algorithm for recovering depth information from farmland road images, which would provide a reliable path for visual navigation. The mean image of pixel value in five channels (R, G, B, S and V) were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm, for achieving precise location of the feature points and ensuring the uniformity and density of the feature points. The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45o in eight directions was then treated as the feature vector, and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points. In order to achieve the depth information of farmland road images, the energy method of feature points was used for eliminating the mismatched points. Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48% and 5.6 s, respectively, with the accuracy for depth recovery of –7.17%-2.97% in a certain sight distance. The mean uniformity, time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%, 5.0-6.5 s, and higher than 88%, respectively, indicating that performance for achieving the feature points (e.g., uniformity, matching accuracy, and algorithm real-time) of the improved SIFT algorithm were superior to that of conventional SIFT algorithm. This study provides an important reference for navigation technology of agricultural equipment based on machine vision. Keywords: scale-invariant feature transform (sift), feature matching, canny operator, energy method of feature point, farmland road, depth recovery, visual navigation DOI: 10.25165/j.ijabe.20191204.4821 Citation: Yao L J, Hu D, Yang Z D, Li H B, Qian M B. Depth recovery for unstructured farmland road image using an improved SIFT algorithm. Int J Agric & Biol Eng, 2019; 12(4): 141–147.

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

A new indexing system for multiresolution image retrieval has been developed. The system analyzes not only whole images but also--for the first time--local features present in the images. This capability is important in managing large-scale image systems such as video databases and medical image databases. Key to the method is the use of an innovative wavelet, called the W-transform. This transform is characterized by its ability to handle signals of arbitrary size. Equally important, the W-transform is not constrained by assumptions about periodicity. That is, the transformed signal in some location is related only to the signals at its neighboring location. These properties make the W-transform a natural tool for analyzing local image features. A brief discussion is given of how the system works. The feature-oriented image indexing and retrieval method offers two major advantages over traditional multiresolution histogram comparison methods. The response time for retrieving image coarse components is considerably faster than that for retrieving whole images--thereby dramatically speeding image retrieval over the Ethernet. Moreover, the new method can be used to search images that are significantly different from the query image as a whole but contain local features identical with or similar to those in the query image.

In view of the problem that the LIOP feature description algorithm based on global gray-scale sequence mode makes insufficient use of local patch information and the feature dimension and the number of sampling points increase exponentially when constructing feature descriptors, an improved algorithm is proposed. Firstly, LIOP detection operator is used to extract the local features of the image, and the local features are preliminarily matched. Then, the operator is used to detect the feature points in the scale space, and the obtained feature points are grouped according to the local area features of the image, and the matching operation of feature point sets is carried out in the local area that has been successfully matched. It is insensitive to image rotation and spatial changes, and can adaptively aggregate coding features according to multi-resolution features of images. Color images are classified by texture and scene. Experiments show that this method improves the description ability of existing LIOP methods for color images.