40,031 research outputs found
Image retrieval with hierarchical matching pursuit
A novel representation of images for image retrieval is introduced in this
paper, by using a new type of feature with remarkable discriminative power.
Despite the multi-scale nature of objects, most existing models perform feature
extraction on a fixed scale, which will inevitably degrade the performance of
the whole system. Motivated by this, we introduce a hierarchical sparse coding
architecture for image retrieval to explore multi-scale cues. Sparse codes
extracted on lower layers are transmitted to higher layers recursively. With
this mechanism, cues from different scales are fused. Experiments on the
Holidays dataset show that the proposed method achieves an excellent retrieval
performance with a small code length.Comment: 5 pages, 6 figures, conferenc
Current Chinese bryological literature (4)
According to our collections of literature, about 400 scientific papers dealing with Chinese bryophytes have been published in China and abroad during 1990’s. Among these, more than 50 % were published in different scientific journals in China and often written in Chinese with English abstract, which are not well known and assessable for foreign bryologists. Therefore, in addition to previous Chinese literature I-III (Cao et al. 1990, Li et Zhang 1993, 1994), we present the fourth part of Chinese literature herewith. It is hoped that this up-dated list will provide useful information for all people who are interested in bryological research
Optimality of Graphlet Screening in High Dimensional Variable Selection
Consider a linear regression model where the design matrix X has n rows and p
columns. We assume (a) p is much large than n, (b) the coefficient vector beta
is sparse in the sense that only a small fraction of its coordinates is
nonzero, and (c) the Gram matrix G = X'X is sparse in the sense that each row
has relatively few large coordinates (diagonals of G are normalized to 1).
The sparsity in G naturally induces the sparsity of the so-called graph of
strong dependence (GOSD). We find an interesting interplay between the signal
sparsity and the graph sparsity, which ensures that in a broad context, the set
of true signals decompose into many different small-size components of GOSD,
where different components are disconnected.
We propose Graphlet Screening (GS) as a new approach to variable selection,
which is a two-stage Screen and Clean method. The key methodological innovation
of GS is to use GOSD to guide both the screening and cleaning. Compared to
m-variate brute-forth screening that has a computational cost of p^m, the GS
only has a computational cost of p (up to some multi-log(p) factors) in
screening.
We measure the performance of any variable selection procedure by the minimax
Hamming distance. We show that in a very broad class of situations, GS achieves
the optimal rate of convergence in terms of the Hamming distance. Somewhat
surprisingly, the well-known procedures subset selection and the lasso are rate
non-optimal, even in very simple settings and even when their tuning parameters
are ideally set
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data
Topic modeling based on latent Dirichlet allocation (LDA) has been a
framework of choice to deal with multimodal data, such as in image annotation
tasks. Another popular approach to model the multimodal data is through deep
neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type
of topic model called the Document Neural Autoregressive Distribution Estimator
(DocNADE) was proposed and demonstrated state-of-the-art performance for text
document modeling. In this work, we show how to successfully apply and extend
this model to multimodal data, such as simultaneous image classification and
annotation. First, we propose SupDocNADE, a supervised extension of DocNADE,
that increases the discriminative power of the learned hidden topic features
and show how to employ it to learn a joint representation from image visual
words, annotation words and class label information. We test our model on the
LabelMe and UIUC-Sports data sets and show that it compares favorably to other
topic models. Second, we propose a deep extension of our model and provide an
efficient way of training the deep model. Experimental results show that our
deep model outperforms its shallow version and reaches state-of-the-art
performance on the Multimedia Information Retrieval (MIR) Flickr data set.Comment: 24 pages, 10 figures. A version has been accepted by TPAMI on Aug
4th, 2015. Add footnote about how to train the model in practice in Section
5.1. arXiv admin note: substantial text overlap with arXiv:1305.530
The Financial Deepening-Productivity Nexus in China: 1987-2001
The financial intermediation-growth nexus is a widely studied topic in the literature of development economics. Deepening financial intermediation may promote economic growth by mobilizing more investments, and lifting returns to financial resources, which raises productivity. Relying on provincial panel data from China, this paper attempts to examine if regional productivity growth is accounted for by the deepening process of financial development. Towards this end, an appropriate measurement of financial depth is constructed and then included as a determinant of productivity growth. It finds that a significant and positive nexus exists between financial deepening and productivity growth. Given the divergent pattern of financial deepening between coastal and inland provinces, this finding also helps explain the rising regional disparity in China.growth, financial development, productivity, China
A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation
Topic modeling based on latent Dirichlet allocation (LDA) has been a
framework of choice to perform scene recognition and annotation. Recently, a
new type of topic model called the Document Neural Autoregressive Distribution
Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance
for document modeling. In this work, we show how to successfully apply and
extend this model to the context of visual scene modeling. Specifically, we
propose SupDocNADE, a supervised extension of DocNADE, that increases the
discriminative power of the hidden topic features by incorporating label
information into the training objective of the model. We also describe how to
leverage information about the spatial position of the visual words and how to
embed additional image annotations, so as to simultaneously perform image
classification and annotation. We test our model on the Scene15, LabelMe and
UIUC-Sports datasets and show that it compares favorably to other topic models
such as the supervised variant of LDA.Comment: 13 pages, 5 figure
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