How automated image analysis techniques help scientists in species identification and classification?

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre­ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef­forts on identification of species include specimens’ image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179–193

Automated identification of moth species

In this paper, Digital Automated Identification System (DAISY) was used to identify species of local moths. 210 species of super family Bombycoidea from Moth of Borneo (Part 3) were trained in DAISY. The overall identification of Moths gave a fairly accurate retrieval, with F1= 0.81

Identification of selected monogeneans using image processing, artificial neural network and K-nearest neighbor

Over the last two decades, improvements in developing computational tools have made significant contributions to the classification of images of biological specimens to their corresponding species. These days, identification of biological species is much easier for taxonomists and even non-taxonomists due to the development of automated computer techniques and systems. In this study, we developed a fully automated identification model for monogenean images based on the shape characters of the haptoral organs of eight species: Sinodiplectanotrema malayanum, Diplectanum jaculator, Trianchoratus pahangensis, Trianchoratus lonianchoratus, Trianchoratus malayensis, Metahaliotrema ypsilocleithru, Metahaliotrema mizellei and Metahaliotrema similis. Linear Discriminant Analysis (LDA) method was used to reduce the dimension of extracted feature vectors which were then used in the classification with K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) classifiers for the identification of monogenean specimens of eight species. The need for the discovery of new characters for identification of species has been acknowledged for log by systematic parasitology. Using the overall form of anchors and bars for extraction of features led to acceptable results in automated classification of monogeneans. To date, this is the first fully automated identification model for monogeneans with an accuracy of 86.25% using KNN and 93.1% using ANN

Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research—Individual identification and attribute prediction with image features from deep neural networks and decoupled decision models applied to elephants and great apes

Animal re-identification based on image data, either recorded manually by photographers or automatically with camera traps, is an important task for ecological studies about biodiversity and conservation that can be highly automatized with algorithms from computer vision and machine learning. However, fixed identification models only trained with standard datasets before their application will quickly reach their limits, especially for long-term monitoring with changing environmental conditions, varying visual appearances of individuals over time that differ a lot from those in the training data, and new occurring individuals that have not been observed before. Hence, we believe that active learning with human-in-the-loop and continuous lifelong learning is important to tackle these challenges and to obtain high-performance recognition systems when dealing with huge amounts of additional data that become available during the application. Our general approach with image features from deep neural networks and decoupled decision models can be applied to many different mammalian species and is perfectly suited for continuous improvements of the recognition systems via lifelong learning. In our identification experiments, we consider four different taxa, namely two elephant species: African forest elephants and Asian elephants, as well as two species of great apes: gorillas and chimpanzees. Going beyond classical re-identification, our decoupled approach can also be used for predicting attributes of individuals such as gender or age using classification or regression methods. Although applicable for small datasets of individuals as well, we argue that even better recognition performance will be achieved by improving decision models gradually via lifelong learning to exploit huge datasets and continuous recordings from long-term applications. We highlight that algorithms for deploying lifelong learning in real observational studies exist and are ready for use. Hence, lifelong learning might become a valuable concept that supports practitioners when analyzing large-scale image data during long-term monitoring of mammals

Cognitive and Neurobiological Degeneration of the Mental Lexicon in Primary Progressive Aphasia

The ease with which we use the thousands of words in our vocabulary stands in stark contrast to our difficulty establishing how they are organized in our mind and brain. The breakdown of language due to cortical atrophy in primary progressive aphasia (PPA) creates conditions to study this organization at a cognitive and neurobiological level in that the three variants of this disease, namely non-fluent, logopenic, and semantic PPA, each bear their own signature of language-specific decline and cortical atrophy. As the impaired regions in each variant are linked to different lexical and semantic attributes of words, lexical decision performance of individuals with the distinct variants can reveal the conceptual and neural architecture of the lexicon through an anatomical-behavioral relationship. This dissertation investigated which lexical and semantic factors influence the structural degeneration of word processing in individuals with each variant of PPA through three studies that focused on the role of general semantic knowledge, psycholinguistic variables, and sensory-perceptual features, respectively. In Study 1, 41 individuals with PPA (13 non-fluent, 14 logopenic, and 14 semantic) as well as healthy controls (N = 25) performed a lexical decision task that consisted of 355 real words, carefully controlled on a broad range of psycholinguistic and semantic variables, and 175 pseudowords matched with the real words on the psycholinguistic variables. Two additional non-verbal semantic tasks (Pyramids and Palm Trees test and Over-regular Object Test) were administered to assess semantic ability and its relation with lexical decision performance. Results showed that—contrary to diagnostic expectations for the PPA variants—all three groups of individuals with PPA scored below the performance of matched control participants. The lexical-decision performance across all individuals with PPA correlated with semantic ability, but this correlation was not significant when separately analyzed per diagnosis. These findings suggest that semantic ability plays an active role in word recognition, but is not essential to lexical-semantic processing. In Study 2, the performance of the same participants was analyzed on a selected subset of the 355 words to examine the differential influence of the psycholinguistic factors lexical frequency, age of acquisition, and neighborhood density on lexical-semantic processing across the three diagnostic groups. The results demonstrated that lexical frequency has the largest influence on lexical-semantic processing, but that independent of that, age of acquisition and neighborhood density also play a role. The effect of these two variables becomes more salient dependent on the variant of PPA, accordant to the patterns of atrophy. That is, individuals with non-fluent and logopenic PPA experienced a neighborhood density effect consistent with atrophy in the inferior frontal and temporoparietal cortices, associated with lexical analysis and word form processing. By contrast, individuals with semantic PPA experienced an age of acquisition effect consistent with atrophy in the anterior temporal lobe which has been associated with semantic processing in previous literature. These findings suggest that the degeneration of lexical-semantic processing is affected by lexical factors—which relate to language-specific brain regions—in line with a hierarchical mental lexicon structure, such that a selective deficit at one of the levels of the mental lexicon results in distinctively expressed effects among psycholinguistic variables. Study 3 employed voxel-based morphometry (VBM) to identify the association between cortical volume—measured through T1-weighted magnetic resonance images (MRI)—and lexical decision performance related to sensory-perceptual features in 37 of the individuals with PPA and 17 of the controls on a second subset of the 355 words. Results showed that at both behavioral and neurobiological levels, semantic sensory-perceptual features of words (a strong association with, e.g., sound or action) influence lexical decision performance across all three groups with PPA. The results highlight the roles of the right hemisphere, the cerebellum, and the anterior temporal lobe in processing various sensory-perceptual features of concepts. The anterior temporal lobe has been proposed to be a semantic hub which processes various sensory-perceptual features (‘spokes’) into a conceptual representation in the hub-and-spoke model. The current results confirm this hub-role of the anterior temporal lobe, as well as the link of the ‘spokes’ to sensory-perceptual brain regions, as proposed by the hypothesis of embodied cognition. Most importantly, the results suggest that the intensity of semantic processing in the anterior temporal lobe is regulated by the degree of association with sensory-perceptual information. The current research presents novel evidence that lexical-semantic processing is influenced by a combination of lexical and semantic factors at both conceptual and neurobiological levels, which can become impaired in different ways in individuals with PPA based on a set of anatomical-behavioral relationships. In particular, this dissertation broke new ground in demonstrating that the intensity of semantic processing in the anterior temporal lobe depends on the degree of sensory-perceptual information of concepts, supporting both the hub-and-spoke model and the hypothesis of embodied cognition. As well, this dissertation established the independent effects of lexical frequency from age of acquisition and neighborhood density and their roles in lexical-semantic decline in PPA, supporting the theory of hierarchical distinctions between lexemes and their conceptual representations in the mental lexicon

Grounding semantic cognition using computational modelling and network analysis

The overarching objective of this thesis is to further the field of grounded semantics using a range of computational and empirical studies. Over the past thirty years, there have been many algorithmic advances in the modelling of semantic cognition. A commonality across these cognitive models is a reliance on hand-engineering “toy-models”. Despite incorporating newer techniques (e.g. Long short-term memory), the model inputs remain unchanged. We argue that the inputs to these traditional semantic models have little resemblance with real human experiences. In this dissertation, we ground our neural network models by training them with real-world visual scenes using naturalistic photographs. Our approach is an alternative to both hand-coded features and embodied raw sensorimotor signals. We conceptually replicate the mutually reinforcing nature of hybrid (feature-based and grounded) representations using silhouettes of concrete concepts as model inputs. We next gradually develop a novel grounded cognitive semantic representation which we call scene2vec, starting with object co-occurrences and then adding emotions and language-based tags. Limitations of our scene-based representation are identified for more abstract concepts (e.g. freedom). We further present a large-scale human semantics study, which reveals small-world semantic network topologies are context-dependent and that scenes are the most dominant cognitive dimension. This finding leads us to conclude that there is no meaning without context. Lastly, scene2vec shows promising human-like context-sensitive stereotypes (e.g. gender role bias), and we explore how such stereotypes are reduced by targeted debiasing. In conclusion, this thesis provides support for a novel computational viewpoint on investigating meaning - scene-based grounded semantics. Future research scaling scene-based semantic models to human-levels through virtual grounding has the potential to unearth new insights into the human mind and concurrently lead to advancements in artificial general intelligence by enabling robots, embodied or otherwise, to acquire and represent meaning directly from the environment

Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry

Domain-sensitive topic management in a modular conversational agent framework

Flexible nontask-oriented conversational agents require content for generating responses and mechanisms that serve them for choosing appropriate topics to drive interactions with users. Structured knowledge resources such as ontologies are a useful mechanism to represent conversational topics. In order to develop the topic-management mechanism, we addressed a number of research issues related to the development of the required infrastructure. First, we address the issue of heavy human involvement in the construction of knowledge resources by proposing a four-stage automatic process for building domain-specific ontologies. These ontologies are comprised of a set of subtaxonomies obtained from WordNet, an electronic dictionary that arranges concepts in a hierarchical structure. The roots of these subtaxonomies are obtained from Wikipedia’s article links or wikilinks; this under the hypothesis that wikilinks provide a sense of relatedness from the article consulted to their destinations. With the knowledge structures defined, we explore the possibility of using semantic relatedness over these domain-specific ontologies as a mean to propose conversational topics in a coherent manner. For this, we examine different automatic measures of semantic relatedness to determine which correlates with human judgements obtained from an automatically constructed dataset. We then examine the question of whether domain information influences the human perception of semantic relatedness in a way that automatic measures do not replicate. This study requires us to design and implement a process to build datasets with pairs of concepts as those used in the literature to evaluate automatic measures of semantic relatedness, but with domain information associated. This study shows, to statistical significance, that existing measures of semantic relatedness do not take domain into consideration, and that including domain as a factor in this calculation can enhance the agreement of automatic measures with human assessments. Finally, this artificially constructed measure is integrated into the Toy’s dialogue manager, in order to help in the real-time selection of conversational topics. This supplements our result that the use of semantic relatedness seems to produce more coherent and interesting topic transitions than existing mechanisms