53,323 research outputs found
Compositional Sketch Search
We present an algorithm for searching image collections using free-hand
sketches that describe the appearance and relative positions of multiple
objects. Sketch based image retrieval (SBIR) methods predominantly match
queries containing a single, dominant object invariant to its position within
an image. Our work exploits drawings as a concise and intuitive representation
for specifying entire scene compositions. We train a convolutional neural
network (CNN) to encode masked visual features from sketched objects, pooling
these into a spatial descriptor encoding the spatial relationships and
appearances of objects in the composition. Training the CNN backbone as a
Siamese network under triplet loss yields a metric search embedding for
measuring compositional similarity which may be efficiently leveraged for
visual search by applying product quantization.Comment: ICIP 2021 camera-ready versio
Part-based Multi-stream Model for Vehicle Searching
Due to the enormous requirement in public security and intelligent
transportation system, searching an identical vehicle has become more and more
important. Current studies usually treat vehicle as an integral object and then
train a distance metric to measure the similarity among vehicles. However,
these raw images may be exactly similar to ones with different identification
and include some pixels in background that may disturb the distance metric
learning. In this paper, we propose a novel and useful method to segment an
original vehicle image into several discriminative foreground parts, and these
parts consist of some fine grained regions that are named discriminative
patches. After that, these parts combined with the raw image are fed into the
proposed deep learning network. We can easily measure the similarity of two
vehicle images by computing the Euclidean distance of the features from FC
layer. Two main contributions of this paper are as follows. Firstly, a method
is proposed to estimate if a patch in a raw vehicle image is discriminative or
not. Secondly, a new Part-based Multi-Stream Model (PMSM) is designed and
optimized for vehicle retrieval and re-identification tasks. We evaluate the
proposed method on the VehicleID dataset, and the experimental results show
that our method can outperform the baseline.Comment: Published in International Conference on Pattern Recognition 201
An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration
We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images
Using Apache Lucene to Search Vector of Locally Aggregated Descriptors
Surrogate Text Representation (STR) is a profitable solution to efficient
similarity search on metric space using conventional text search engines, such
as Apache Lucene. This technique is based on comparing the permutations of some
reference objects in place of the original metric distance. However, the
Achilles heel of STR approach is the need to reorder the result set of the
search according to the metric distance. This forces to use a support database
to store the original objects, which requires efficient random I/O on a fast
secondary memory (such as flash-based storages). In this paper, we propose to
extend the Surrogate Text Representation to specifically address a class of
visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD).
This approach is based on representing the individual sub-vectors forming the
VLAD vector with the STR, providing a finer representation of the vector and
enabling us to get rid of the reordering phase. The experiments on a publicly
available dataset show that the extended STR outperforms the baseline STR
achieving satisfactory performance near to the one obtained with the original
VLAD vectors.Comment: In Proceedings of the 11th Joint Conference on Computer Vision,
Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) -
Volume 4: VISAPP, p. 383-39
Cluster Oriented Image Retrieval System with Context Based Color Feature Subspace Selection
This paper presents a cluster oriented image retrieval system with context recognition mechanism for selection subspaces of color features. Our idea to implement a context in the image retrieval system is how to recognize the most important features in the image search by connecting the user impression to the query. We apply a context recognition with Mathematical Model of Meaning (MMM) and then make a projection to the color features with a color impression metric. After a user gives a context, the MMM retrieves the highest correlated words to the context. These representative words are projected to the color impression metric to obtain the most significant colors for subspace feature selection. After applying subspace selection, the system then clusters the image database using Pillar-Kmeans algorithm. The centroids of clustering results are used for calculating the similarity measurements to the image query. We perform our proposed system for experimental purpose with the Ukiyo-e image datasets from Tokyo Metropolitan Library for representing the Japanese cultural image collections
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