33,719 research outputs found
RBIR Based on Signature Graph
This paper approaches the image retrieval system on the base of visual
features local region RBIR (region-based image retrieval). First of all, the
paper presents a method for extracting the interest points based on
Harris-Laplace to create the feature region of the image. Next, in order to
reduce the storage space and speed up query image, the paper builds the binary
signature structure to describe the visual content of image. Based on the
image's binary signature, the paper builds the SG (signature graph) to classify
and store image's binary signatures. Since then, the paper builds the image
retrieval algorithm on SG through the similar measure EMD (earth mover's
distance) between the image's binary signatures. Last but not least, the paper
gives an image retrieval model RBIR, experiments and assesses the image
retrieval method on Corel image database over 10,000 images.Comment: 4 pages, 4 figure
Content Based Image Retrieval Berdasarkan Ciri Tekstur Menggunakan Wavelet
Content Based Image Retrieval System (CBIR) merupakan suatu metode pencarian citra dengan melakukan perbandingan antara citra query dengan citra yang ada didatabase berdasarkan informasi yang ada pada citra tersebut (Query by Example). Metode CBIR yang sering digunakan adalah pencarian berdasarkan kemiripan warna, bentuk, dan tekstur,Pada penelitian kali ini akan digunakan metode pencarian citra berdasarkan kemiripan tekstur dengan menggunakan wavelet. Jenis wavelet yang digunakan adalah Haar wavelet dengan fungsi dekomposisinya, diharapkan metode dengan wavelet ini memungkinkan pencarian citra dapat dilakukan dengan hasil yang baik khususnya citra yang berbasis tekstur
Unsupervised Triplet Hashing for Fast Image Retrieval
Hashing has played a pivotal role in large-scale image retrieval. With the
development of Convolutional Neural Network (CNN), hashing learning has shown
great promise. But existing methods are mostly tuned for classification, which
are not optimized for retrieval tasks, especially for instance-level retrieval.
In this study, we propose a novel hashing method for large-scale image
retrieval. Considering the difficulty in obtaining labeled datasets for image
retrieval task in large scale, we propose a novel CNN-based unsupervised
hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised
hashing network is designed under the following three principles: 1) more
discriminative representations for image retrieval; 2) minimum quantization
loss between the original real-valued feature descriptors and the learned hash
codes; 3) maximum information entropy for the learned hash codes. Extensive
experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH
outperforms several state-of-the-art unsupervised hashing methods in terms of
retrieval accuracy
User experiments with the Eurovision cross-language image retrieval system
In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval.
The system is evaluated by multilingual users for two search tasks with the system configured in
English and five other languages. To our knowledge this is the first published set of user
experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual
search engine using little knowledge of any language other than English, (2) categorizing images
assists the user's search, and (3) there are differences in the way users search between the proposed
search tasks. Based on the two search tasks and user feedback, we describe important aspects of
any CL image retrieval system
Region-Based Image Retrieval Revisited
Region-based image retrieval (RBIR) technique is revisited. In early attempts
at RBIR in the late 90s, researchers found many ways to specify region-based
queries and spatial relationships; however, the way to characterize the
regions, such as by using color histograms, were very poor at that time. Here,
we revisit RBIR by incorporating semantic specification of objects and
intuitive specification of spatial relationships. Our contributions are the
following. First, to support multiple aspects of semantic object specification
(category, instance, and attribute), we propose a multitask CNN feature that
allows us to use deep learning technique and to jointly handle multi-aspect
object specification. Second, to help users specify spatial relationships among
objects in an intuitive way, we propose recommendation techniques of spatial
relationships. In particular, by mining the search results, a system can
recommend feasible spatial relationships among the objects. The system also can
recommend likely spatial relationships by assigned object category names based
on language prior. Moreover, object-level inverted indexing supports very fast
shortlist generation, and re-ranking based on spatial constraints provides
users with instant RBIR experiences.Comment: To appear in ACM Multimedia 2017 (Oral
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