22,057 research outputs found
Cluster Oriented Image Retrieval System with Context Based Color Feature Subspace Selection
This paper presents a cluster oriented image retrieval system with context recognition mechanism for selection subspaces of color features. Our idea to implement a context in the image retrieval system is how to recognize the most important features in the image search by connecting the user impression to the query. We apply a context recognition with Mathematical Model of Meaning (MMM) and then make a projection to the color features with a color impression metric. After a user gives a context, the MMM retrieves the highest correlated words to the context. These representative words are projected to the color impression metric to obtain the most significant colors for subspace feature selection. After applying subspace selection, the system then clusters the image database using Pillar-Kmeans algorithm. The centroids of clustering results are used for calculating the similarity measurements to the image query. We perform our proposed system for experimental purpose with the Ukiyo-e image datasets from Tokyo Metropolitan Library for representing the Japanese cultural image collections
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
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