845 research outputs found
Embedding based on function approximation for large scale image search
The objective of this paper is to design an embedding method that maps local
features describing an image (e.g. SIFT) to a higher dimensional representation
useful for the image retrieval problem. First, motivated by the relationship
between the linear approximation of a nonlinear function in high dimensional
space and the stateof-the-art feature representation used in image retrieval,
i.e., VLAD, we propose a new approach for the approximation. The embedded
vectors resulted by the function approximation process are then aggregated to
form a single representation for image retrieval. Second, in order to make the
proposed embedding method applicable to large scale problem, we further derive
its fast version in which the embedded vectors can be efficiently computed,
i.e., in the closed-form. We compare the proposed embedding methods with the
state of the art in the context of image search under various settings: when
the images are represented by medium length vectors, short vectors, or binary
vectors. The experimental results show that the proposed embedding methods
outperform existing the state of the art on the standard public image retrieval
benchmarks.Comment: Accepted to TPAMI 2017. The implementation and precomputed features
of the proposed F-FAemb are released at the following link:
http://tinyurl.com/F-FAem
Offline Writer Identification Using Convolutional Neural Network Activation Features
Convolutional neural networks (CNNs) have recently become the
state-of-the-art tool for large-scale image classification. In this work we
propose the use of activation features from CNNs as local descriptors for
writer identification. A global descriptor is then formed by means of GMM
supervector encoding, which is further improved by normalization with the
KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR
2013 benchmark database and the CVL dataset. While we perform comparably to the
state of the art on CVL, our proposed method yields about 0.21 absolute
improvement in terms of mAP on the challenging bilingual ICDAR dataset.Comment: fixed tab 1
Exploiting Local Features from Deep Networks for Image Retrieval
Deep convolutional neural networks have been successfully applied to image
classification tasks. When these same networks have been applied to image
retrieval, the assumption has been made that the last layers would give the
best performance, as they do in classification. We show that for instance-level
image retrieval, lower layers often perform better than the last layers in
convolutional neural networks. We present an approach for extracting
convolutional features from different layers of the networks, and adopt VLAD
encoding to encode features into a single vector for each image. We investigate
the effect of different layers and scales of input images on the performance of
convolutional features using the recent deep networks OxfordNet and GoogLeNet.
Experiments demonstrate that intermediate layers or higher layers with finer
scales produce better results for image retrieval, compared to the last layer.
When using compressed 128-D VLAD descriptors, our method obtains
state-of-the-art results and outperforms other VLAD and CNN based approaches on
two out of three test datasets. Our work provides guidance for transferring
deep networks trained on image classification to image retrieval tasks.Comment: CVPR DeepVision Workshop 201
- …