Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images
We attack the problem of learning concepts automatically from noisy web image search results. Going beyond low level attributes, such as colour and texture, we explore weakly-labelled datasets for the learning of higher level concepts, such as scene categories. The idea is based on discovering common characteristics shared among subsets of images by posing a method that is able to organise the data while eliminating irrelevant instances. We propose a novel clustering and outlier detection method, namely Rectifying Self Organizing Maps (RSOM). Given an image collection returned for a concept query, RSOM provides clusters pruned from outliers. Each cluster is used to train a model representing a different characteristics of the concept. The proposed method outperforms the state-of-the-art studies on the task of learning low-level concepts, and it is competitive in learning higher level concepts as well. It is capable to work at large scale with no supervision through exploiting the available sources.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringImage RetrievalOutlier DetectionSimilar Papers 제목 키워드 기반
TaBIIC2: Interactive Building of Ontological Taxonomies using Weighted Self-Organizing Maps
Ontologies represent the conceptual knowledge of a domain. At the core of an ontology is the taxonomy of concepts and subconcepts that represent specific entities, which can be complex to build. In many cases, informatio…
Finding Concept Representations in Neural Networks with Self-Organizing Maps
In sufficiently complex tasks, it is expected that as a side effect of learning to solve a problem, a neural network will learn relevant abstractions of the representation of that problem. This has been confirmed in part…
Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis
The quality of datasets plays a crucial role in the successful training and deployment of deep learning models. Especially in the medical field, where system performance may impact the health of patients, clean datasets …
Outlier DetectionOut-of-Distribution DetectionQuantizationMulti-label Stream Classification with Self-Organizing Maps
Several learning algorithms have been proposed for offline multi-label classification. However, applications in areas such as traffic monitoring, social networks, and sensors produce data continuously, the so called data…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1A Self-Organizing Network with Varying Density Structure for Characterizing Sensorimotor Transformations in Robotic Systems
In this work, we present the development of a neuro-inspired approach for characterizing sensorimotor relations in robotic systems. The proposed method has self-organizing and associative properties that enable it to aut…