We introduce BAFT, a fast binary and quasi affine invariant local image feature. It combines the affine invariance of Harris Affine feature descriptors with the speed of binary descriptors such as BRISK and ORB. BAFT derives its speed and precision from sampling local image patches in a pattern that depends on the second moment matrix of the same image patch. This approach results in a fast but discriminative descriptor, especially for image pairs with large perspective changes. Our evaluation on 40 different image pairs shows that BAFT increases the area under the precision/recall curve (AUC) compared to traditional descriptors for the majority of image pairs. In addition we show that this improvement comes with a very low performance penalty compared to the similar ORB descriptor. The BAFT source code is available for download.

We present a crowdsourcing (CS) study to examine how specific attributes probabilistically affect the selection and sequencing of images from personal photo collections. Thirteen image attributes are explored, including seven people-centric properties. We first propose a novel dataset shaping technique based on mixed integer linear programming (MILP) to identify a subset of photos in which the attributes of interest are uniformly distributed and minimally correlated. Shaping enables the synthesis of compact, balanced, and representative datasets for CS, and facilitates effective learning of the selection likelihood of an image as well as its relative position in a sequence, given its attributes. We further present an ILP-based slideshow creation framework to select and arrange (a subset of) appealing images from a personal photo library. Quantitative and qualitative evaluations confirm that our method outperforms regression-based and greedy approaches for photo selection and sequencing, generating slideshows similar in quality to those created by humans.

Today's cameras produce images that often exceed 10 megapixels. Yet computing and matching local features for images of this size can easily take 20 seconds or more using optimized matching algorithms. This is much too slow for interactive applications and much too expensive for large scale image operations. We introduce Fast-Match, an algorithm designed to match large images efficiently without compromising matching accuracy. It derives its speed from only computing features in those parts of the image that can be confidently matched. Fast-Match is an order of magnitude faster than the popular Ratio-Match, yet often doubles matching precision for difficult image pairs.