A new SAR image registration method was Proposed based on improved SIFT algorithm. Which adopted multi-core system platform was used to overcoming the problem of high complexity algorithm of SIFT algorithm; According to the characteristics of SAR image, first of all, the source SAR image was enhanced in airspace, and finish the parallel extraction of feature points with the improved SIFT algori...

This paper presents a query-by-example word spotting in handwritten Arabic documents, based on Scale Invariant Feature Transform (SIFT), without using any text word or line segmentation approach, because any errors affect to the subsequent word representation. First the interest points are automatically extracted from the images using SIFT detector, then, we use SIFT descriptor to represent eac...

Transmission and storage of local feature descriptors are of critical importance for mobile visual search applications. We perform a comprehensive survey of Scale Invariant Feature Transform (SIFT) compression schemes proposed in the literature and evaluate them in a common framework. Further, we compare the different schemes to the recently proposed low bit-rate Compressed Histogram of Gradien...

We investigate transform coding to efficiently store and transmit SIFT and SURF image descriptors. We show that image and feature matching algorithms are robust to significantly compressed features. We achieve nearperfect image matching and retrieval for both SIFT and SURF using ∼2 bits/dimension. When applied to SIFT and SURF, this provides a 16× compression relative to conventional floating p...

Dermatologists consider color as one of the major discriminative aspects of melanoma. In this paper we evaluate the importance of color in the keypoint detection and description steps of the Bag-of-Features model. We compare the performance of gray scale against that of color sampling methods using Harris Laplace detector and its color extensions. Moreover, we compare the performance of SIFT an...

For many years, various local descriptors that are insensitive to geometric changes such as viewpoint, rotation, and scale changes, have been attracting attention due to their promising performance. However, most existing local descriptors including the SIFT (Scale Invariant Feature Transform) are based on luminance information rather than color information thereby resulting in instability to p...

Convolutional Neural Network (NN) has gained a lot of attention of the researchers due to its high accuracy in classification and feature learning. In this paper, we evaluated the performance of CNN used as feature for image retrieval with the gold standard feature, aka SIFT. Experiments are conducted on famous Oxford 5k data-set. The mAP of SIFT and CNN is 0.6279 and 0.5284, respectively. The ...

Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspo...