6,858 research outputs found
Object Level Deep Feature Pooling for Compact Image Representation
Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.Comment: Deep Vision 201
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convolutional Neural Networks (CNN). Unfortunately, such approaches require
vast amounts of computational power and memory, preventing efficient
implementations on embedded systems. This work proposes a CNN-based detector,
adapting a general-purpose convolutional network to the task at hand. By
thoroughly analyzing and optimizing each step of the detection pipeline, we
develop an architecture that outperforms methods based on traditional image
features and achieves an accuracy close to the state-of-the-art while having
low computational complexity. Furthermore, the model is compressed in order to
fit the tight constrains of low power devices with a limited amount of embedded
memory available. This paper makes two main contributions: (1) it proves that a
region based deep neural network can be finely tuned to achieve adequate
accuracy for pedestrian detection (2) it achieves a very low memory usage
without reducing detection accuracy on the Caltech Pedestrian dataset.Comment: IEEE 2016 ICCE-Berli
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval
In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from
the information loss during quantization, another cause is that the SIFT
feature only describes the local gradient distribution. To address this
problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform
feature fusion at indexing level. Basically, complementary features are coupled
into a multi-dimensional inverted index. Each dimension of c-MI corresponds to
one kind of feature, and the retrieval process votes for images similar in both
SIFT and other feature spaces. Specifically, we exploit the fusion of local
color feature into c-MI. While the precision of visual match is greatly
enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation
of SIFT and color features significantly reduces the impact of false positive
matches.
Extensive experiments on several benchmark datasets demonstrate that c-MI
improves the retrieval accuracy significantly, while consuming only half of the
query time compared to the baseline. Importantly, we show that c-MI is well
complementary to many prior techniques. Assembling these methods, we have
obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench
datasets, respectively, which compare favorably with the state-of-the-arts.Comment: 8 pages, 7 figures, 6 tables. Accepted to CVPR 201
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