5,168 research outputs found
Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two components:
An inverted file index to restrict the actual search to only a subset that
should contain most of the items relevant to the query; An approximate distance
computation mechanism to rapidly scan these lists. While supervised deep
learning has recently enabled improvements to the latter, the former continues
to be based on unsupervised clustering in the literature. In this work, we
propose a first system that learns both components within a unifying neural
framework of structured binary encoding
Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two components:
An inverted file index to restrict the actual search to only a subset that
should contain most of the items relevant to the query; An approximate distance
computation mechanism to rapidly scan these lists. While supervised deep
learning has recently enabled improvements to the latter, the former continues
to be based on unsupervised clustering in the literature. In this work, we
propose a first system that learns both components within a unifying neural
framework of structured binary encoding
Coding local and global binary visual features extracted from video sequences
Binary local features represent an effective alternative to real-valued
descriptors, leading to comparable results for many visual analysis tasks,
while being characterized by significantly lower computational complexity and
memory requirements. When dealing with large collections, a more compact
representation based on global features is often preferred, which can be
obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW)
model. Several applications, including for example visual sensor networks and
mobile augmented reality, require visual features to be transmitted over a
bandwidth-limited network, thus calling for coding techniques that aim at
reducing the required bit budget, while attaining a target level of efficiency.
In this paper we investigate a coding scheme tailored to both local and global
binary features, which aims at exploiting both spatial and temporal redundancy
by means of intra- and inter-frame coding. In this respect, the proposed coding
scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC)
paradigm. That is, visual features are extracted from the acquired content,
encoded at remote nodes, and finally transmitted to a central controller that
performs visual analysis. This is in contrast with the traditional approach, in
which visual content is acquired at a node, compressed and then sent to a
central unit for further processing, according to the Compress-Then-Analyze
(CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of
rate-efficiency curves in the context of two different visual analysis tasks:
homography estimation and content-based retrieval. Our results show that the
novel ATC paradigm based on the proposed coding primitives can be competitive
with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search
Retrieval pipelines commonly rely on a term-based search to obtain candidate
records, which are subsequently re-ranked. Some candidates are missed by this
approach, e.g., due to a vocabulary mismatch. We address this issue by
replacing the term-based search with a generic k-NN retrieval algorithm, where
a similarity function can take into account subtle term associations. While an
exact brute-force k-NN search using this similarity function is slow, we
demonstrate that an approximate algorithm can be nearly two orders of magnitude
faster at the expense of only a small loss in accuracy. A retrieval pipeline
using an approximate k-NN search can be more effective and efficient than the
term-based pipeline. This opens up new possibilities for designing effective
retrieval pipelines. Our software (including data-generating code) and
derivative data based on the Stack Overflow collection is available online
A study into annotation ranking metrics in geo-tagged image corpora
Community contributed datasets are becoming increasingly common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient due to the high frequency of common landmark tags within the data set and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf
to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task
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