Image Understanding using Self-similar SIFT Features
نویسندگان
چکیده
In this paper we present a new method to group self-similar SIFT features in images. The aim is to automatically build groups of all SIFT features with the same semantics in an image. To achieve this a new distance between SIFT feature vectors taking into account their orientation and scale is introduced. The methods are presented in the context of recognition of buildings. A first evaluation shows promising results.
منابع مشابه
Performance Evaluation of Local Detectors in the Presence of Noise for Multi-Sensor Remote Sensing Image Matching
Automatic, efficient, accurate, and stable image matching is one of the most critical issues in remote sensing, photogrammetry, and machine vision. In recent decades, various algorithms have been proposed based on the feature-based framework, which concentrates on detecting and describing local features. Understanding the characteristics of different matching algorithms in various applications ...
متن کاملA Survey of Content-Based Image Retrieval Systems using Scale-Invariant Feature Transform (SIFT)
Content-based image retrieval (CBIR) is a method for finding similar images from large image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. In recent years, local descriptors are used as image features to improve the performance of CBIR. The SIFT is one of the most loc...
متن کاملLocal Image Descriptor using VQ-SIFT for Image Retrieval
In this paper, we present local image descriptor using VQ-SIFT for more effective and efficient image retrieval. Instead of SIFT's weighted orientation histograms, we apply vector quantization (VQ) histogram as an alternate representation for SIFT features. Experimental results show that SIFT features using VQ-based local descriptors can achieve better image retrieval accuracy than the conventi...
متن کاملSIFT Texture Description for Understanding Breast Ultrasound Images
Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regul...
متن کاملPerformance evaluation of block-based copy- move image forgery detection algorithms
Copy-move forgery is a particular type of distortion where a part or portions of one image is/are copied to other parts of the same image. This type of manipulation is done to hide a particular part of the image or to copy one or more objects into the same image. There are several methods for detecting copy-move forgery, including block-based and key point-based methods. In this paper, a method...
متن کامل