Geometric Verification Method Research Articles

Computer-aided diagnosis of mammographic masses using local geometric constraint image retrieval

Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is gradually increasing. Hence, in mammographic mass retrieval, the method of exploiting the vocabulary tree framework and the inverted file has been proven to have high accuracy and excellent scalability. However, it only considered the features in each image as a visual word and ignored the spatial configurations of features, which greatly affects the retrieval performance. To overcome this drawback, we adopt the geometric verification method in mammographic mass retrieval. First, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. Then, we grasp local similarity characteristics of deformations within the local regions by constructing the circle regions of corresponding pairs. Meanwhile, we quarter the circle to express the geometric relationship of local matches in the area and strictly generate the spatial encoding. Additionally, we rotate local matches in the area to control the strictness of geometric constraints. Finally, we verify geometric consistency to filter the false matches.The experimental results demonstrate that our method could significantly improve the retrieval accuracy with low computational cost.

The Shape Interaction Matrix-Based Affine Invariant Mismatch Removal for Partial-Duplicate Image Search.

Mismatch removal is a key step in many computer vision problems. In this paper, we handle the mismatch removal problem by adopting shape interaction matrix (SIM). Given the homogeneous coordinates of the two corresponding point sets, we first compute the SIMs of the two point sets. Then, we detect the mismatches by picking out the most different entries between the two SIMs. Even under strong affine transformations, outliers, noises, and burstiness, our method can still work well. Actually, this paper is the first non-iterative mismatch removal method that achieves affine invariance. Extensive results on synthetic 2D points matching data sets and real image matching data sets verify the effectiveness, efficiency, and robustness of our method in removing mismatches. Moreover, when applied to partial-duplicate image search, our method reaches higher retrieval precisions with shorter time cost compared with the state-of-the-art geometric verification methods.

Object recognition using local invariant features for robotic applications: A survey

The main goal of this survey is to present a complete analysis of object recognition methods based on local invariant features from a robotics perspective; a summary which can be used by developers of robot vision applications in the selection and development of object recognition systems. The survey includes a brief description of the main approaches reported in the literature, with more specific analyses of local interest point computation methods, local descriptor computation and matching methods, and geometric verification methods. Different methods are analyzed by considering the main requirements of robotics applications, such as real-time operation with limited on-board computational resources, and constrained observational conditions derived from the robot geometry (e.g. limited camera resolution). In addition, various object recognition systems are evaluated in a service-robot domestic environment, where the final task to be performed by a service robot is the manipulation of objects. It can be concluded from the results reported that (i) the most suitable keypoint detectors are ORB, BRISK, Fast Hessian, and DoG, (ii) the most suitable descriptors are ORB, BRISK, SIFT, and SURF, (iii) the final performance of object recognition systems using local invariant features under real-world conditions depends strongly on the geometric verification methods being used, and (iv) the best performing object recognition systems are built using ORB–ORB and DoG–SIFT keypoint–descriptor combinations. ORB–ORB based systems are faster, while DoG–SIFT are more robust to real-world conditions.

Hierarchical Geometry Verification via Maximum Entropy Saliency in Image Retrieval

We propose a new geometric verification method in image retrieval—Hierarchical Geometry Verification via Maximum Entropy Saliency (HGV)—which aims at filtering the redundant matches and remaining the information of retrieval target in images which is partly out of the salient regions with hierarchical saliency and also fully exploring the geometric context of all visual words in images. First of all, we obtain hierarchical salient regions of a query image based on the maximum entropy principle and label visual features with salient tags. The tags added to the feature descriptors are used to compute the saliency matching score, and the scores are regarded as the weight information in the geometry verification step. Second we define a spatial pattern as a triangle composed of three matched features and evaluate the similarity between every two spatial patterns. Finally, we sum all spatial matching scores with weights to generate the final ranking list. Experiment results prove that Hierarchical Geometry Verification based on Maximum Entropy Saliency can not only improve retrieval accuracy, but also reduce the time consumption of the full retrieval.

SIFT match verification by geometric coding for large-scale partial-duplicate web image search

Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visual-words model does not capture the geometric context among local features in images well, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing methods on global geometric verification are either computationally expensive to ensure real-time response, or cannot handle rotation well. To solve the preceding problems, in this article, we propose a novel geometric coding algorithm, to encode the spatial context among local features for large-scale partial-duplicate Web image retrieval. Our geometric coding consists of geometric square coding and geometric fan coding, which describe the spatial relationships of SIFT features into three geo-maps for global verification to remove geometrically inconsistent SIFT matches. Our approach is not only computationally efficient, but also effective in detecting partial-duplicate images with rotation, scale changes, partial-occlusion, and background clutter. Experiments in partial-duplicate Web image search, using two datasets with one million Web images as distractors, reveal that our approach outperforms the baseline bag-of-visual-words approach even following a RANSAC verification in mean average precision. Besides, our approach achieves comparable performance to other state-of-the-art global geometric verification methods, for example, spatial coding scheme, but is more computationally efficient.