69,033 research outputs found
Deformable Prototypes for Encoding Shape Categories in Image Databases
We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.Office of Naval Research (Young Investigator Award N00014-06-1-0661
Group Invariant Deep Representations for Image Instance Retrieval
Most image instance retrieval pipelines are based on comparison of vectors
known as global image descriptors between a query image and the database
images. Due to their success in large scale image classification,
representations extracted from Convolutional Neural Networks (CNN) are quickly
gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors
for image instance retrieval. While CNN-based descriptors are generally
remarked for good retrieval performance at lower bitrates, they nevertheless
present a number of drawbacks including the lack of robustness to common object
transformations such as rotations compared with their interest point based FV
counterparts.
In this paper, we propose a method for computing invariant global descriptors
from CNNs. Our method implements a recently proposed mathematical theory for
invariance in a sensory cortex modeled as a feedforward neural network. The
resulting global descriptors can be made invariant to multiple arbitrary
transformation groups while retaining good discriminativeness.
Based on a thorough empirical evaluation using several publicly available
datasets, we show that our method is able to significantly and consistently
improve retrieval results every time a new type of invariance is incorporated.
We also show that our method which has few parameters is not prone to
overfitting: improvements generalize well across datasets with different
properties with regard to invariances. Finally, we show that our descriptors
are able to compare favourably to other state-of-the-art compact descriptors in
similar bitranges, exceeding the highest retrieval results reported in the
literature on some datasets. A dedicated dimensionality reduction step
--quantization or hashing-- may be able to further improve the competitiveness
of the descriptors
Autoencoding the Retrieval Relevance of Medical Images
Content-based image retrieval (CBIR) of medical images is a crucial task that
can contribute to a more reliable diagnosis if applied to big data. Recent
advances in feature extraction and classification have enormously improved CBIR
results for digital images. However, considering the increasing accessibility
of big data in medical imaging, we are still in need of reducing both memory
requirements and computational expenses of image retrieval systems. This work
proposes to exclude the features of image blocks that exhibit a low encoding
error when learned by a autoencoder (). We examine the
histogram of autoendcoding errors of image blocks for each image class to
facilitate the decision which image regions, or roughly what percentage of an
image perhaps, shall be declared relevant for the retrieval task. This leads to
reduction of feature dimensionality and speeds up the retrieval process. To
validate the proposed scheme, we employ local binary patterns (LBP) and support
vector machines (SVM) which are both well-established approaches in CBIR
research community. As well, we use IRMA dataset with 14,410 x-ray images as
test data. The results show that the dimensionality of annotated feature
vectors can be reduced by up to 50% resulting in speedups greater than 27% at
expense of less than 1% decrease in the accuracy of retrieval when validating
the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image
Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015,
Orleans, Franc
Stochastic accumulation of feature information in perception and memory
It is now well established that the time course of perceptual processing influences the first second or so of performance in a wide variety of cognitive tasks. Over the last20 years, there has been a shift from modeling the speed at which a display is processed, to modeling the speed at which different features of the display are perceived and formalizing how this perceptual information is used in decision making. The first of these models(Lamberts, 1995) was implemented to fit the time course of performance in a speeded perceptual categorization task and assumed a simple stochastic accumulation of feature information. Subsequently, similar approaches have been used to model performance in a range of cognitive tasks including identification, absolute identification, perceptual matching, recognition, visual search, and word processing, again assuming a simple stochastic accumulation of feature information from both the stimulus and representations held in memory. These models are typically fit to data from signal-to-respond experiments whereby the effects of stimulus exposure duration on performance are examined, but response times (RTs) and RT distributions have also been modeled. In this article, we review this approach and explore the insights it has provided about the interplay between perceptual processing, memory retrieval, and decision making in a variety of tasks. In so doing, we highlight how such approaches can continue to usefully contribute to our understanding of cognition
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