A Kind of Affine Weighted Moment Invariants

A new kind of geometric invariants is proposed in this paper, which is called affine weighted moment invariant (AWMI). By combination of local affine differential invariants and a framework of global integral, they can more effectively extract features of images and help to increase the number of low-order invariants and to decrease the calculating cost. The experimental results show that AWMIs have good stability and distinguishability and achieve better results in image retrieval than traditional moment invariants. An extension to 3D is straightforward

Hausdorff-Distance Enhanced Matching of Scale Invariant Feature Transform Descriptors in Context of Image Querying

Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a robust while computationally efficient process of matching feature descriptors for image-to-image querying in standards datasets. For this purpose, Scale Invariant Feature Transform (SIFT) descriptors have been matched using our presented algorithm, followed by the computation of our related similarity measure. This approach has shown excellent performance in both retrieval accuracy and speed

MapSnapper: Engineering an Efficient Algorithm for Matching Images of Maps from Mobile Phones

The MapSnapper project aimed to develop a system for robust matching of low-quality images of a paper map taken from a mobile phone against a high quality digital raster representation of the same map. The paper presents a novel methodology for performing content-based image retrieval and object recognition from query images that have been degraded by noise and subjected to transformations through the imaging system. In addition the paper also provides an insight into the evaluation-driven development process that was used to incrementally improve the matching performance until the design specifications were met

A New Ranking Approach and a Revisited Ratio Test for Improving Content-Based Image Retrieval

Geometric Verification (GV) is the last step for most visual search systems. It consists of two parts: first, ratio test is used to find matches between feature descriptors; second, a geometric consistency check is applied. Both steps are computationally expensive, but all the attempts made to speed up the process deal with the geometric check part only. In this work, we focus indeed on ratio test. Using simple PCA and other tricks, a speed-up of an order of magnitude is achieved preserving good retrieval accuracy. Moreover, we propose a modified ranking approach which exploits distance information between descriptors and further improves retrieval performance