556 research outputs found
Cross-Domain Image Retrieval with Attention Modeling
With the proliferation of e-commerce websites and the ubiquitousness of smart
phones, cross-domain image retrieval using images taken by smart phones as
queries to search products on e-commerce websites is emerging as a popular
application. One challenge of this task is to locate the attention of both the
query and database images. In particular, database images, e.g. of fashion
products, on e-commerce websites are typically displayed with other
accessories, and the images taken by users contain noisy background and large
variations in orientation and lighting. Consequently, their attention is
difficult to locate. In this paper, we exploit the rich tag information
available on the e-commerce websites to locate the attention of database
images. For query images, we use each candidate image in the database as the
context to locate the query attention. Novel deep convolutional neural network
architectures, namely TagYNet and CtxYNet, are proposed to learn the attention
weights and then extract effective representations of the images. Experimental
results on public datasets confirm that our approaches have significant
improvement over the existing methods in terms of the retrieval accuracy and
efficiency.Comment: 8 pages with an extra reference pag
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision
and robotics communities, where the task is to accurately and efficiently
recognize the location of a given query image. Despite years of wisdom
accumulated in this field, place recognition still remains an open problem due
to the various ways in which the appearance of real-world places may differ.
This paper presents an overview of the place recognition literature. Since
condition invariant and viewpoint invariant features are essential factors to
long-term robust visual place recognition system, We start with traditional
image description methodology developed in the past, which exploit techniques
from image retrieval field. Recently, the rapid advances of related fields such
as object detection and image classification have inspired a new technique to
improve visual place recognition system, i.e., convolutional neural networks
(CNNs). Thus we then introduce recent progress of visual place recognition
system based on CNNs to automatically learn better image representations for
places. Eventually, we close with discussions and future work of place
recognition.Comment: Applied Sciences (2018
06171 Abstracts Collection -- Content-Based Retrieval
From 23.04.06 to 28.04.06, the Dagstuhl Seminar 06171 `Content-Based Retrieval\u27\u27
was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
BilVideo-7 : video parsing, indexing and retrieval
Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Ph. D.) -- Bilkent University, 2010.Includes bibliographical references leaves 91-103.Video indexing and retrieval aims to provide fast, natural and intuitive access to
large video collections. This is getting more and more important as the amount of
video data increases at a stunning rate. This thesis introduces the BilVideo-7 system
to address the issues related to video parsing, indexing and retrieval.
BilVideo-7 is a distributed and MPEG-7 compatible video indexing and retrieval
system that supports complex multimodal queries in a unified framework. The video
data model is based on an MPEG-7 profile which is designed to represent the videos
by decomposing them into Shots, Keyframes, Still Regions and Moving Regions. The
MPEG-7 compatible XML representations of videos according to this profile are obtained
by the MPEG-7 compatible video feature extraction and annotation tool of
BilVideo-7, and stored in a native XML database. Users can formulate text, color,
texture, shape, location, motion and spatio-temporal queries on an intuitive, easy-touse
visual query interface, whose composite query interface can be used to formulate
very complex queries containing any type and number of video segments with their
descriptors and specifying the spatio-temporal relations between them. The multithreaded
query processing server parses incoming queries into subqueries and executes
each subquery in a separate thread. Then, it fuses subquery results in a bottom-up manner
to obtain the final query result and sends the result to the originating client. The
whole system is unique in that it provides very powerful querying capabilities with a
wide range of descriptors and multimodal query processing in an MPEG-7 compatible
interoperable environment.Baştan, MuhammetPh.D
Bridging the semantic gap in content-based image retrieval.
To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques
Saliency for Image Description and Retrieval
We live in a world where we are surrounded by ever increasing numbers of images. More often than not, these images have very little metadata by which they can be indexed and searched. In order to avoid information overload, techniques need to be developed to enable these image collections to be searched by their content. Much of the previous work on image retrieval has used global features such as colour and texture to describe the content of the image. However, these global features are insufficient to accurately describe the image content when different parts of the image have different characteristics. This thesis initially discusses how this problem can be circumvented by using salient interest regions to select the areas of the image that are most interesting and generate local descriptors to describe the image characteristics in that region. The thesis discusses a number of different saliency detectors that are suitable for robust retrieval purposes and performs a comparison between a number of these region detectors. The thesis then discusses how salient regions can be used for image retrieval using a number of techniques, but most importantly, two techniques inspired from the field of textual information retrieval. Using these robust retrieval techniques, a new paradigm in image retrieval is discussed, whereby the retrieval takes place on a mobile device using a query image captured by a built-in camera. This paradigm is demonstrated in the context of an art gallery, in which the device can be used to find more information about particular images. The final chapter of the thesis discusses some approaches to bridging the semantic gap in image retrieval. The chapter explores ways in which un-annotated image collections can be searched by keyword. Two techniques are discussed; the first explicitly attempts to automatically annotate the un-annotated images so that the automatically applied annotations can be used for searching. The second approach does not try to explicitly annotate images, but rather, through the use of linear algebra, it attempts to create a semantic space in which images and keywords are positioned such that images are close to the keywords that represent them within the space
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