8,961 research outputs found
Recent Advance in Content-based Image Retrieval: A Literature Survey
The explosive increase and ubiquitous accessibility of visual data on the Web
have led to the prosperity of research activity in image search or retrieval.
With the ignorance of visual content as a ranking clue, methods with text
search techniques for visual retrieval may suffer inconsistency between the
text words and visual content. Content-based image retrieval (CBIR), which
makes use of the representation of visual content to identify relevant images,
has attracted sustained attention in recent two decades. Such a problem is
challenging due to the intention gap and the semantic gap problems. Numerous
techniques have been developed for content-based image retrieval in the last
decade. The purpose of this paper is to categorize and evaluate those
algorithms proposed during the period of 2003 to 2016. We conclude with several
promising directions for future research.Comment: 22 page
PFO: A Parallel Friendly High Performance System for Online Query and Update of Nearest Neighbors
Nearest Neighbor(s) search is the fundamental computational primitive to
tackle massive dataset. Locality Sensitive Hashing (LSH) has been a bracing
tool for Nearest Neighbor(s) search in high dimensional spaces. However,
traditional LSH systems cannot be applied in online big data systems to handle
a large volume of query/update requests, because most of the systems optimize
the query efficiency with the assumption of infrequent updates and missing the
parallel-friendly design. As a result, the state-of-the-art LSH systems cannot
adapt the system response to the user behavior interactively.
In this paper, we propose a new LSH system called PFO. It handles
query/update requests in RAM and scales the system capacity by using flash
memory. To achieve high streaming data throughput, PFO adopts a
parallel-friendly indexing structure while preserving the distance between data
points. Further, it accommodates inbound data in real-time and dispatches
update requests intelligently to eliminate the cross-threads synchronization.
We carried out extensive evaluations with large synthetic and standard
benchmark datasets. Results demonstrate that PFO delivers shorter latency and
offers scalable capacity compared with the existing LSH systems. PFO serves
with higher throughput than the state-of-the-art LSH indexing structure when
dealing with online query/update requests to nearest neighbors. Meanwhile, PFO
returns neighbors with much better quality, thus being efficient to handle
online big data applications, e.g. streaming recommendation system, interactive
machine learning systems
Indexing of CNN Features for Large Scale Image Search
The convolutional neural network (CNN) features can give a good description
of image content, which usually represent images with unique global vectors.
Although they are compact compared to local descriptors, they still cannot
efficiently deal with large-scale image retrieval due to the cost of the linear
incremental computation and storage. To address this issue, we build a simple
but effective indexing framework based on inverted table, which significantly
decreases both the search time and memory usage. In addition, several
strategies are fully investigated under an indexing framework to adapt it to
CNN features and compensate for quantization errors. First, we use multiple
assignment for the query and database images to increase the probability of
relevant images' co-existing in the same Voronoi cells obtained via the
clustering algorithm. Then, we introduce embedding codes to further improve
precision by removing false matches during a search. We demonstrate that by
using hashing schemes to calculate the embedding codes and by changing the
ranking rule, indexing framework speeds can be greatly improved. Extensive
experiments conducted on several unsupervised and supervised benchmarks support
these results and the superiority of the proposed indexing framework. We also
provide a fair comparison between the popular CNN features.Comment: 21 pages, 9 figures, submitted to Multimedia Tools and Application
Exquisitor: Interactive Learning at Large
Increasing scale is a dominant trend in today's multimedia collections, which
especially impacts interactive applications. To facilitate interactive
exploration of large multimedia collections, new approaches are needed that are
capable of learning on the fly new analytic categories based on the visual and
textual content. To facilitate general use on standard desktops, laptops, and
mobile devices, they must furthermore work with limited computing resources. We
present Exquisitor, a highly scalable interactive learning approach, capable of
intelligent exploration of the large-scale YFCC100M image collection with
extremely efficient responses from the interactive classifier. Based on
relevance feedback from the user on previously suggested items, Exquisitor uses
semantic features, extracted from both visual and text attributes, to suggest
relevant media items to the user. Exquisitor builds upon the state of the art
in large-scale data representation, compression and indexing, introducing a
cluster-based retrieval mechanism that facilitates the efficient suggestions.
With Exquisitor, each interaction round over the full YFCC100M collection is
completed in less than 0.3 seconds using a single CPU core. That is 4x less
time using 16x smaller computational resources than the most efficient
state-of-the-art method, with a positive impact on result quality. These
results open up many interesting research avenues, both for exploration of
industry-scale media collections and for media exploration on mobile devices
Efficient Similarity Indexing and Searching in High Dimensions
Efficient indexing and searching of high dimensional data has been an area of
active research due to the growing exploitation of high dimensional data and
the vulnerability of traditional search methods to the curse of dimensionality.
This paper presents a new approach for fast and effective searching and
indexing of high dimensional features using random partitions of the feature
space. Experiments on both handwritten digits and 3-D shape descriptors have
shown the proposed algorithm to be highly effective and efficient in indexing
and searching real data sets of several hundred dimensions. We also compare its
performance to that of the state-of-the-art locality sensitive hashing
algorithm
ENIGMAWatch: ProofWatch Meets ENIGMA
In this work we describe a new learning-based proof guidance -- ENIGMAWatch
-- for saturation-style first-order theorem provers. ENIGMAWatch combines two
guiding approaches for the given-clause selection implemented for the E ATP
system: ProofWatch and ENIGMA. ProofWatch is motivated by the watchlist (hints)
method and based on symbolic matching of multiple related proofs, while ENIGMA
is based on statistical machine learning. The two methods are combined by using
the evolving information about symbolic proof matching as an additional
information that characterizes the saturation-style proof search for the
statistical learning methods. The new system is experimentally evaluated on a
large set of problems from the Mizar Library. We show that the added
proof-matching information is considered important by the statistical machine
learners, and that it leads to improvements in E's Performance over ProofWatch
and ENIGMA.Comment: 12 pages, 5 tables, 3 figures, submitted to TABLEAUX 201
A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency
As cloud computing becomes prevalent in recent years, more and more
enterprises and individuals outsource their data to cloud servers. To avoid
privacy leaks, outsourced data usually is encrypted before being sent to cloud
servers, which disables traditional search schemes for plain text. To meet both
end of security and searchability, search-supported encryption is proposed.
However, many previous schemes suffer severe vulnerability when typos and
semantic diversity exist in query requests. To overcome such flaw, higher
error-tolerance is always expected for search-supported encryption design,
sometimes defined as 'fuzzy search'. In this paper, we propose a new scheme of
multi-keyword fuzzy search over encrypted and outsourced data. Our approach
introduces a new mechanism to map a natural language expression into a
word-vector space. Compared with previous approaches, our design shows higher
robustness when multiple kinds of typos are involved. Besides, our approach is
enhanced with novel data structures to improve search efficiency. These two
innovations can work well for both accuracy and efficiency. Moreover, these
designs will not hurt the fundamental security. Experiments on a real-world
dataset demonstrate the effectiveness of our proposed approach, which
outperforms currently popular approaches focusing on similar tasks.Comment: 14 pages, 14 figure
Identifying User Intent and Context in Graph Queries
Graph querying is the task of finding similar embeddings of a given query
graph in a large target graph. Existing techniques employ the use of structural
as well as node and edge label similarities to find matches of a query in the
target graph. However, these techniques have ignored the presence of context
(usually manifested in the form of node/edge label distributions) in the query.
In this paper, we propose CGQ (Contextual Graph Querying), a context-aware
subgraph matching technique for querying real-world graphs. We introduce a
novel sub-graph searching paradigm, which involves learning the context
prevalent in the query graph. Under the proposed paradigm, we formulate the
most contextually-similar subgraph querying problem that, given a query graph
and a target graph, aims to identify the (top-k) maximal common subgraph(s)
between the query and the target graphs with the highest contextual similarity.
The quality of a match is quantized using our proposed contextual similarity
function. We prove that the problem is NP-hard and also hard to approximate.
Therefore, to efficiently solve the problem, we propose a hierarchical index,
CGQ-Tree, with its associated search algorithm. Our experiments show that the
proposed CGQ index is effective, efficient and scalable.Comment: 16 page
An Efficient Approach for Geo-Multimedia Cross-Modal Retrieval
Due to the rapid development of mobile Internet techniques, cloud computation
and popularity of online social networking and location-based services, massive
amount of multimedia data with geographical information is generated and
uploaded to the Internet. In this paper, we propose a novel type of cross-modal
multimedia retrieval called geo-multimedia cross-modal retrieval which aims to
search out a set of geo-multimedia objects based on geographical distance
proximity and semantic similarity between different modalities. Previous
studies for cross-modal retrieval and spatial keyword search cannot address
this problem effectively because they do not consider multimedia data with
geo-tags and do not focus on this type of query. In order to address this
problem efficiently, we present the definition of NN geo-multimedia
cross-modal query at the first time and introduce relevant conceptions such as
cross-modal semantic representation space. To bridge the semantic gap between
different modalities, we propose a method named cross-modal semantic matching
which contains two important component, i.e., CorrProj and LogsTran, which aims
to construct a common semantic representation space for cross-modal semantic
similarity measurement. Besides, we designed a framework based on deep learning
techniques to implement common semantic representation space construction. In
addition, a novel hybrid indexing structure named GMR-Tree combining
geo-multimedia data and R-Tree is presented and a efficient NN search
algorithm called GMCMS is designed. Comprehensive experimental evaluation on
real and synthetic dataset clearly demonstrates that our solution outperforms
the-state-of-the-art methods.Comment: 27 page
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