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

Sparse Binary Features for Image Classification

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes improvements based on an efficient descriptor representation. The efficient representation is created using dimensionality reduction techniques, entropy analysis and decorrelated sampling. In a second part, the problem of image classification is tackled. The traditional approach uses machine learning algorithms to create classifiers, and some works already propose to use a compact image representation using feature extraction as preprocessing. The second contribution of this work is to show that binary features, while being very compact and low dimensional (compared to traditional representation of images), still provide a very high discriminant power. This is shown using various learning algorithms and binary descriptors. These years a scheme has been widely used to perform object recognition on images, or equivalently image classification. It is based on the concept of Bag of Visual Words. More precisely, an image is described using an unordered set of visual words, that are generally represented by feature descriptions. The last contribution of this work is to use binary features with a simple Bag of Visual Words classifier. Tests of performance for the image classification are performed on a large database of images

Mobile Media Distribution in Developing Contexts

There are a growing number of mobile phones being used in developing contexts, such as Africa. A large percentage of these phones have the capability to take photographs and transmit them freely using Bluetooth. In order to provide people with media on their mobile phones public displays are becoming more common. Three problems with current public displays – cost, security and mobility – are discussed and system proposed that uses a mobile phone as a server. Media is displayed on specially designed paper posters, which users can photograph using their mobile phones. The resulting photographs are sent, via Bluetooth, to the server, which analyses them in order to locate a specially designed barcode, representing the media, which is then decoded and the requisite media returned to the user. Two barcoding systems are tested in laboratory conditions, and a binary system is found to perform best. The system is then deployed on a campus transportation system to test the effects of motion. The results show that the system is not yet ready for deployment on moving transport