931 research outputs found
Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation
The task of a visual landmark recognition system is to identify photographed
buildings or objects in query photos and to provide the user with relevant
information on them. With their increasing coverage of the world's landmark
buildings and objects, Internet photo collections are now being used as a
source for building such systems in a fully automatic fashion. This process
typically consists of three steps: clustering large amounts of images by the
objects they depict; determining object names from user-provided tags; and
building a robust, compact, and efficient recognition index. To this date,
however, there is little empirical information on how well current approaches
for those steps perform in a large-scale open-set mining and recognition task.
Furthermore, there is little empirical information on how recognition
performance varies for different types of landmark objects and where there is
still potential for improvement. With this paper, we intend to fill these gaps.
Using a dataset of 500k images from Paris, we analyze each component of the
landmark recognition pipeline in order to answer the following questions: How
many and what kinds of objects can be discovered automatically? How can we best
use the resulting image clusters to recognize the object in a query? How can
the object be efficiently represented in memory for recognition? How reliably
can semantic information be extracted? And finally: What are the limiting
factors in the resulting pipeline from query to semantics? We evaluate how
different choices of methods and parameters for the individual pipeline steps
affect overall system performance and examine their effects for different query
categories such as buildings, paintings or sculptures
Incremental Tag Suggestion for Landmark Image Collections
In recent social media applications, descriptive information is collected through user tagging, such as face recognition, and automatic environment sensing, such as GPS. There are many applications that recognize landmarks using information gathered from GPS data. However, GPS is dependent on the location of the camera, not the landmark. In this research, we propose an automatic landmark tagging scheme using secondary regions to distinguish between similar landmarks. We propose two algorithms: 1) landmark tagging by secondary objects and 2) automatic new landmark recognition. Images of 30 famous landmarks from various public databases were used in our experiment. Results show increments of tagged areas and the improvement of landmark tagging accuracy
Fast Approximate -Means via Cluster Closures
-means, a simple and effective clustering algorithm, is one of the most
widely used algorithms in multimedia and computer vision community. Traditional
-means is an iterative algorithm---in each iteration new cluster centers are
computed and each data point is re-assigned to its nearest center. The cluster
re-assignment step becomes prohibitively expensive when the number of data
points and cluster centers are large.
In this paper, we propose a novel approximate -means algorithm to greatly
reduce the computational complexity in the assignment step. Our approach is
motivated by the observation that most active points changing their cluster
assignments at each iteration are located on or near cluster boundaries. The
idea is to efficiently identify those active points by pre-assembling the data
into groups of neighboring points using multiple random spatial partition
trees, and to use the neighborhood information to construct a closure for each
cluster, in such a way only a small number of cluster candidates need to be
considered when assigning a data point to its nearest cluster. Using complexity
analysis, image data clustering, and applications to image retrieval, we show
that our approach out-performs state-of-the-art approximate -means
algorithms in terms of clustering quality and efficiency
Efficient video collection association using geometry-aware Bag-of-Iconics representations
Abstract Recent years have witnessed the dramatic evolution in visual data volume and processing capabilities. For example, technical advances have enabled 3D modeling from large-scale crowdsourced photo collections. Compared to static image datasets, exploration and exploitation of Internet video collections are still largely unsolved. To address this challenge, we first propose to represent video contents using a histogram representation of iconic imagery attained from relevant visual datasets. We then develop a data-driven framework for a fully unsupervised extraction of such representations. Our novel Bag-of-Iconics (BoI) representation efficiently analyzes individual videos within a large-scale video collection. We demonstrate our proposed BoI representation with two novel applications: (1) finding video sequences connecting adjacent landmarks and aligning reconstructed 3D models and (2) retrieving geometrically relevant clips from video collections. Results on crowdsourced datasets illustrate the efficiency and effectiveness of our proposed Bag-of-Iconics representation
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