A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semi-supervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) i2015FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP [2013/08645-0, 2013/50169-1]CNPq [306580/2012-8, 484254/2012-0]2013/08645-0; 2013/50169-1306580/2012-8;484254/2012-0SEM INFORMAÇÃ

Machine Learning to Dissipate a Cyclone

Generally, the present disclosure is directed to using machine learning to dissipate or otherwise combat a cyclone. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict characteristics of a cyclone (or infant cyclone which can be referred to as a cyclet) such as location, path, strength, number of anti-cyclone devices needed to combat the cyclone, positioning of the anti-cyclone devices, etc. based on information descriptive of the cyclone including imagery of the cyclone or other collected data such as, for example, wind speed and water temperature

Three Essays on Railroad Safety Analysis Using Non-Parametric Statistical Methods

he FRA mandated railroad companies to install a new monitoring system known as Positive Train Control (PTC). This system overlays sensors, signals, and transponders over existing track and other wayside infrastructure. Technologists designed the system to prevent accidents mainly caused by human negligence and communications. However, PTC will not address track-related defects, which is the second dominant cause of accidents. A new track monitoring system called Railway Autonomous Inspection Localization System (RAILS) was proposed to address track-related accidents. RAILS is based on low-cost sensor technology that identifies defect symptoms, ranks their severity, classifies defect types, and localizes their positions. So, RAILS technology can augment the PTC by identifying track-related issues. The main objectives of this dissertation are: (1) To compare the potential performance of RAILS with traditional inspection methods based on its fundamental theory of operation; (2) To identify factors contributing to railroad accidents; and (3) To determine and rank factors responsible for severe financial damages caused by railroad accidents.The first two objectives will help compare the proposed technology and identify the major factors responsible for causing train accidents. The final objective will help to categorize accidents based on the potential financial damage severity. Categorizing such incidents would help to create a database that prioritizes issues and suggest possible countermeasure based on the problems. The study's key findings are as follows: (1) RAILS is more efficient in conducting continuous inspection and identifying potential defects than traditional systems by 33%, with only two trains per day and a 50% first-pass detection probability; (2) Nonparametric methods provide implicit information about rail accidents and function better than parametric methods by highlighting factors that are responsible for causing accidents rather than identifying the cause-and-effect relationship; (3) The most significant reasons for causing the financial damages are the number of derailed freight cars and the absence of territory signalization; and (4) Nonparametric methods automatically categorize rail accidents and, using text narratives, highlight causative factors responsible for a train derailment

Local selection of features and its applications to image search and annotation

In multimedia applications, direct representations of data objects typically involve hundreds or thousands of features. Given a query object, the similarity between the query object and a database object can be computed as the distance between their feature vectors. The neighborhood of the query object consists of those database objects that are close to the query object. The semantic quality of the neighborhood, which can be measured as the proportion of neighboring objects that share the same class label as the query object, is crucial for many applications, such as content-based image retrieval and automated image annotation. However, due to the existence of noisy or irrelevant features, errors introduced into similarity measurements are detrimental to the neighborhood quality of data objects. One way to alleviate the negative impact of noisy features is to use feature selection techniques in data preprocessing. From the original vector space, feature selection techniques select a subset of features, which can be used subsequently in supervised or unsupervised learning algorithms for better performance. However, their performance on improving the quality of data neighborhoods is rarely evaluated in the literature. In addition, most traditional feature selection techniques are global, in the sense that they compute a single set of features across the entire database. As a consequence, the possibility that the feature importance may vary across different data objects or classes of objects is neglected. To compute a better neighborhood structure for objects in high-dimensional feature spaces, this dissertation proposes several techniques for selecting features that are important to the local neighborhood of individual objects. These techniques are then applied to image applications such as content-based image retrieval and image label propagation. Firstly, an iterative K-NN graph construction method for image databases is proposed. A local variant of the Laplacian Score is designed for the selection of features for individual images. Noisy features are detected and sparsified iteratively from the original standardized feature vectors. This technique is incorporated into an approximate K-NN graph construction method so as to improve the semantic quality of the graph. Secondly, in a content-based image retrieval system, a generalized version of the Laplacian Score is used to compute different feature subspaces for images in the database. For online search, a query image is ranked in the feature spaces of database images. Those database images for which the query image is ranked highly are selected as the query results. Finally, a supervised method for the local selection of image features is proposed, for refining the similarity graph used in an image label propagation framework. By using only the selected features to compute the edges leading from labeled image nodes to unlabeled image nodes, better annotation accuracy can be achieved. Experimental results on several datasets are provided in this dissertation, to demonstrate the effectiveness of the proposed techniques for the local selection of features, and for the image applications under consideration

Weakly Supervised Attention-based Recognition under Spectral, Turbulence, and Resource Variations

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability. This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search process, with dynamic capabilities for computational adjustment based on operational demands or input complexity. The overall framework is demonstrated over biometric tasks, such as person re-identification and facial recognition, and shows superiority in performance and efficiency compared to other unsupervised methods, and in some cases even supervised methods