Scene classification in compressed and constrained domain

Abstract

Holistic representations of natural scenes are an effective and powerful source of information for semantic classification and analysis of images. Despite the technological hardware and software advances, consumer single-sensor imaging devices technology are quite far from the ability of recognising scenes and/or to exploit the visual content during (or after) acquisition time. The frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. The authors exploit a holistic representation of the scene in the discrete cosine transform domain fully compatible with the JPEG format. The advised representation is coupled with a logistic classifier to perform classification of the scene at superordinate level of description (e.g. natural against artificial), or to discriminate between multiple classes of scenes usually acquired by a consumer imaging device (e.g. portrait, landscape and document). The proposed method is able to work in constrained domain. Experiments confirm the effectiveness of the proposed method. The obtained results closely match state-of-the-art methods in terms of accuracy outperforming in terms of computational resources.

Forgery Detection and Value Identification of Euro Banknotes

“…Future work will be devoted to increase the recognition accuracy and to reduce the computational time. Different feature spaces and classification algorithms suitable to work in constrained domains will be tested [8]. Moreover, extension of the current solution to other currency (e.g., US Dollars, UK Pounds, etc.)…”

“…This fact has induced researchers to develop different counterfeit detection algorithms, taking into account the different currencies. Despite different algorithms having been presented in the literature for embedded device systems [7, 8], just a few researchers proposed a full system for both recognition and validation of Euro banknotes considering a constrained domain.…”

Adaptive Scene Category Discovery With Generative Learning and Compositional Sampling

“…Furthermore, Lazebnik et al [14] present a spatial pyramid representation of BoWs by pooling words at different image scales, and this representation effectively improves results for scene categorization [15]. Farinella et al [16] propose to build an effective scene representation based on constrained and compressed domains.…”

On the performances of computer vision algorithms on mobile platforms

“…), novel Computational Photography solutions are available to build appealing imaging applications that cannot be performed before. 1,2 The main idea is to overcome the limitation of traditional imaging devices by using computational methods which can exploit the different inputs offered by a mobile devices (e.g., from low level data, such as Bayer pattern, GPS position, etc.). 3 Since different cameras are usually embedded in devices of new generation, Computer Vision algorithms will be extremely useful in many mobile applications of next future.…”