Recently, the study of lichens has acquired relevance by their potential for production of medicines as well as bioindicators of air quality and ecosystem health. However, computational studies for automatic classification of lichens are few. These investigations depend on availability of large volumes of images for the training process, a scenario quite far from reality. Therefore, it is crucial to identify image descriptors for improving classifier performance, under a limited number of samples. This article introduces a novel method to automatically classify lichens using a descriptor, robust to scale, rotation, and illumination variations, and based on analysis of textures and color. By applying a support vector machine (SVM) classifier, an average F1 score of 99.3% is achieved when classifying lichens in the three categories: crustose, fruticose, and foliose. The method supports the work of taxonomists and facilitates inexperienced users to identify and characterize lichens.

This paper considers the issues of scheduling and caching JPEG2000 data in client/server interactive browsing applications, under memory and channel bandwidth constraints. It analyzes how the conveyed data have to be selected at the server and managed within the client cache so as to maximize the reactivity of the browsing application. Formally, to render the dynamic nature of the browsing session, we assume the existence of a reaction model that defines when the user launches a novel command as a function of the image quality displayed at the client. As a main outcome, our work demonstrates that, due to the latency inherent to client/server exchanges, a priori expectation about future navigation commands may help to improve the overall reactivity of the system. In our study, the browsing session is defined by the evolution of a rectangular window of interest (WoI) along the time. At any given time, the WoI defines the position and the resolution of the image data to display at the client. The expectation about future navigation commands is then formalized based on a stochastic navigation model, which defines the probability that a given WoI is requested next, knowing previous WoI requests. Based on that knowledge, several scheduling scenarios are considered. The first scenario is conventional and transmits all the data corresponding to the current WoI before prefetching the most promising data outside the current WoI. Alternative scenarios are then proposed to anticipate prefetching, by scheduling data expected to be requested in the future before all the current WoI data have been sent out. Our results demonstrate that, for predictable navigation commands, anticipated prefetching improves the overall reactivity of the system by up to 30% compared to the conventional scheduling approach. They also reveal that an accurate knowledge of the reaction model is not required to get these significant improvements.

This paper considers the remote interactive browsing of large JPEG2000 images. In contrast with previous contributions, we focus on the dynamic nature of the system. Practically, we study the conditions under which a priori knowledge about the user behavior may help to improve the browsing system reactivity, when combined with appropriate pre-fetching mechanisms. In particular, our simulations show that, due to the latency inherent to client/server exchanges, a benefit can be drawn by scheduling future window of interest (WoI) data before all current WoI data have been sent to the client. They also reveal that an accurate knowledge of the user behavior is not necessary to get important improvements over conventional scheduling approaches.