Hyperfeatures - multilevel local coding for visual recognition

International audienceHistograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant and have good resistance to local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics at scales larger than their local input patches. We present a new multilevel visual representation, ‘hyperfeatures', that is designed to remedy this. The starting point is the familiar notion that to detect object parts, in practice it often suffices to detect co-occurrences of more local object fragments – a process that can be formalized as comparison (e.g. vector quantization) of image patches against a codebook of known fragments, followed by local aggregation of the resulting codebook membership vectors to detect co-occurrences. This process converts local collections of image descriptor vectors into somewhat less local histogram vectors – higher-level but spatially coarser descriptors. We observe that as the output is again a local descriptor vector, the process can be iterated, and that doing so captures and codes ever larger assemblies of object parts and increasingly abstract or ‘semantic' image properties. We formulate the hyperfeatures model and study its performance under several different image coding methods including clustering based Vector Quantization, Gaussian Mixtures, and combinations of these with Latent Dirichlet Allocation. We find that the resulting high-level features provide improved performance in several object image and texture image classification tasks

Practical Application of Methanol-Mediated Mutualistic Symbiosis between Methylobacterium Species and a Roof Greening Moss, Racomitrium japonicum

Bryophytes, or mosses, are considered the most maintenance-free materials for roof greening. Racomitrium species are most often used due to their high tolerance to desiccation. Because they grow slowly, a technology for forcing their growth is desired. We succeeded in the efficient production of R. japonicum in liquid culture. The structure of the microbial community is crucial to stabilize the culture. A culture-independent technique revealed that the cultures contain methylotrophic bacteria. Using yeast cells that fluoresce in the presence of methanol, methanol emission from the moss was confirmed, suggesting that it is an important carbon and energy source for the bacteria. We isolated Methylobacterium species from the liquid culture and studied their characteristics. The isolates were able to strongly promote the growth of some mosses including R. japonicum and seed plants, but the plant-microbe combination was important, since growth promotion was not uniform across species. One of the isolates, strain 22A, was cultivated with R. japonicum in liquid culture and in a field experiment, resulting in strong growth promotion. Mutualistic symbiosis can thus be utilized for industrial moss production

Free-Shape Polygonal Object Localization

Polygonal objects are prevalent in man-made scenes. Early approaches to detecting them relied mainly on geometry while subsequent ones also incorporated appearance-based cues. It has recently been shown that this could be done fast by searching for cycles in graphs of line-fragments, provided that the cycle scoring function can be expressed as additive terms attached to individual fragments. In this paper, we propose an approach that eliminates this restriction. Given a weighted line-fragment graph, we use its cyclomatic number to partition the graph into managebly-sized sub-graphs that preserve nodes and edges with a high weight and are most likely to contain object contours. Object contours are then detected as maximally scoring elementary circuits enumerated in each sub-graph. Our approach can be used with any cycle scoring function and multiple candidates that share line fragments can be found. This is unlike in other approaches that rely on a greedy approach to finding candidates. We demonstrate that our approach significantly outperforms the state-of-the-art for the detection of building rooftops in aerial images and polygonal object categories from ImageNet

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

Stable Carbon and Nitrogen Isotopes in a Peat Profile Are Influenced by Early Stage Diagenesis and Changes in Atmospheric CO2 and N Deposition

In this study, we test whether the δ13C and δ15N in a peat profile are, respectively, linked to the recent dilution of atmospheric δ13CO2 caused by increased fossil fuel combustion and changes in atmospheric δ15N deposition. We analysed bulk peat and Sphagnum fuscum branch C and N concentrations and bulk peat, S. fuscum branch and Andromeda polifolia leaf δ13C and δ15N from a 30-cm hummock-like peat profile from an Aapa mire in northern Finland. Statistically significant correlations were found between the dilution of atmospheric δ13CO2 and bulk peat δ13C, as well as between historically increasing wet N deposition and bulk peat δ15N. However, these correlations may be affected by early stage kinetic fractionation during decomposition and possibly other processes. We conclude that bulk peat stable carbon and nitrogen isotope ratios may reflect the dilution of atmospheric δ13CO2 and the changes in δ15N deposition, but probably also reflect the effects of early stage kinetic fractionation during diagenesis. This needs to be taken into account when interpreting palaeodata. There is a need for further studies of δ15N profiles in sufficiently old dated cores from sites with different rates of decomposition: These would facilitate more reliable separation of depositional δ15N from patterns caused by other processes

Prospect and potential of Burkholderia sp. against Phytophthora capsici Leonian: a causative agent for foot rot disease of black pepper

Foot rot disease is a very destructive disease in black pepper in Malaysia. It is caused by Phytophthora capsici Leonian, which is a soilborne pathogenic protist (phylum, Oomycota) that infects aerial and subterranean structures of many host plants. This pathogen is a polycyclic, such that multiple cycles of infection and inoculum production occur in a single growing season. It is more prevalent in the tropics because of the favourable environmental conditions. The utilization of plant growth-promoting rhizobacteria (PGPR) as a biological control agent has been successfully implemented in controlling many plant pathogens. Many studies on the exploration of beneficial organisms have been carried out such as Pseudomonas fluorescens, which is one of the best examples used for the control of Fusarium wilt in tomato. Similarly, P. fluorescens is found to be an effective biocontrol agent against the foot rot disease in black pepper. Nowadays there is tremendous novel increase in the species of Burkholderia with either mutualistic or antagonistic interactions in the environment. Burkholderia sp. is an indigenous PGPR capable of producing a large number of commercially important hydrolytic enzymes and bioactive substances that promote plant growth and health; are eco-friendly, biodegradable and specific in their actions; and have a broad spectrum of antimicrobial activity in keeping down the population of phytopathogens, thus playing a great role in promoting sustainable agriculture today. Hence, in this book chapter, the potential applications of Burkholderia sp. to control foot rot disease of black pepper in Malaysia, their control mechanisms, plant growth promotion, commercial potentials and the future prospects as indigenous PGPR were discussed in relation to sustainable agriculture