13,639 research outputs found
Learning and Using Taxonomies For Fast Visual Categorization
The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously N_(cat) = 10^4 - 10^5 visual categories requires sub-linear classification costs. We explore algorithms for automatically building classification trees which have, in principle, log N_(cat) complexity. We find that a greedy algorithm that recursively splits the set of categories into the two minimally confused subsets achieves 5-20 fold speedups at a small cost in classification performance. Our approach is independent of the specific classification algorithm used. A welcome by-product of our algorithm is a very reasonable taxonomy of the Caltech-256 dataset
Unsupervised learning of visual taxonomies
As more images and categories become available, organizing
them becomes crucial. We present a novel statistical
method for organizing a collection of images into a treeshaped
hierarchy. The method employs a non-parametric
Bayesian model and is completely unsupervised. Each image
is associated with a path through a tree. Similar images
share initial segments of their paths and therefore have a
smaller distance from each other. Each internal node in
the hierarchy represents information that is common to images
whose paths pass through that node, thus providing a
compact image representation. Our experiments show that
a disorganized collection of images will be organized into
an intuitive taxonomy. Furthermore, we find that the taxonomy
allows good image categorization and, in this respect,
is superior to the popular LDA model
Taxonomies for Development
{Excerpt} Organizations spend millions of dollars on management systems without commensurate investments in the categorization needed to organize the information they rest on. Taxonomy work is strategic work: it enables efficient and interoperable retrieval and sharing of data, information, and knowledge by building needs and natural workflows in intuitive structures.
Bible readers think that taxonomy is the worldās oldest profession. Whatever the case, the word is now synonymous with any hierarchical system of classification that orders domains of inquiry into groups and signifies natural relationships among these. (A taxonomic scheme is often depicted as a ātreeā and individual taxonomic units as ābranchesā in the tree.) Almost anything can be classified according to some taxonomic scheme. Resulting catalogs provide conceptual frameworks for miscellaneous purposes including knowledge identification, creation, storage, sharing, and use, including related decision making
On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets
Current research in lifelog data has not paid enough attention to analysis of
cognitive activities in comparison to physical activities. We argue that as we
look into the future, wearable devices are going to be cheaper and more
prevalent and textual data will play a more significant role. Data captured by
lifelogging devices will increasingly include speech and text, potentially
useful in analysis of intellectual activities. Analyzing what a person hears,
reads, and sees, we should be able to measure the extent of cognitive activity
devoted to a certain topic or subject by a learner. Test-based lifelog records
can benefit from semantic analysis tools developed for natural language
processing. We show how semantic analysis of such text data can be achieved
through the use of taxonomic subject facets and how these facets might be
useful in quantifying cognitive activity devoted to various topics in a
person's day. We are currently developing a method to automatically create
taxonomic topic vocabularies that can be applied to this detection of
intellectual activity
Semantically Consistent Regularization for Zero-Shot Recognition
The role of semantics in zero-shot learning is considered. The effectiveness
of previous approaches is analyzed according to the form of supervision
provided. While some learn semantics independently, others only supervise the
semantic subspace explained by training classes. Thus, the former is able to
constrain the whole space but lacks the ability to model semantic correlations.
The latter addresses this issue but leaves part of the semantic space
unsupervised. This complementarity is exploited in a new convolutional neural
network (CNN) framework, which proposes the use of semantics as constraints for
recognition.Although a CNN trained for classification has no transfer ability,
this can be encouraged by learning an hidden semantic layer together with a
semantic code for classification. Two forms of semantic constraints are then
introduced. The first is a loss-based regularizer that introduces a
generalization constraint on each semantic predictor. The second is a codeword
regularizer that favors semantic-to-class mappings consistent with prior
semantic knowledge while allowing these to be learned from data. Significant
improvements over the state-of-the-art are achieved on several datasets.Comment: Accepted to CVPR 201
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