6,371 research outputs found
CONSTRUCTING IMAGE-RATING SYSTEM FOR MATERIAL RECLAMATION
Content-basis image retrieval is important option to prevail within the difficulties of previous works and contains attracted an excellent concentration in past decades. The models according to graph-based ranking were mostly analysed and extensively functional in file recovery area. Within our work we concentrate on the novel in addition to efficient graph-based model for content based image retrieval, designed for out-of-sample recovery on extensive databases. We advise a scalable graph-based ranking representation referred to as effective Manifold Ranking, which address weak points of Manifold Ranking from two most significant viewpoints for example scalable graph construction in addition to effective ranking computation. We concentrate on a famous graph-based model known Manifold Ranking that is a well-known graph-based ranking representation that ranks data samples relevant to intrinsic geometrical structure uncovered with a huge data. The suggested model includes two separate stages just like an offline stage for structuring of ranking model plus an online stage for controlling of recent query. Using the suggested system, we are able to handle database by a million images and perform online retrieval inside a short instance
A COSTLESS VIRTUAL RATING METHOD FOR VISUAL SEARCH
Content-basis image retrieval is important option to prevail within the difficulties of previous works and will be offering attracted an excellent concentration in past decades. The models according to graph-based ranking were mostly analysed and extensively functional in recovery area. Within our work we concentrate on the novel additionally to efficient graph-based model for content based image retrieval, produced for out-of-sample recovery on extensive databases. We advise a scalable graph-based ranking representation referred to as effective Manifold Ranking, which address flaws of Manifold Ranking from two most critical viewpoints for example scalable graph construction additionally to effective ranking computation. We concentrate on a famous graph-based model known Manifold Ranking this is often a well-known graph-based ranking representation that ranks data samples tightly associated with intrinsic geometrical structure uncovered obtaining a massive data. The suggested model includes two separate stages similar to an offline stage for structuring of ranking model by getting a web-based stage for controlling of recent query. While using the suggested system, we're outfitted to deal with database getting countless images and perform online retrieval within the short instance
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions
Unsupervised Generative Adversarial Cross-modal Hashing
Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing unsupervised methods learn hashing functions by preserving inter and
intra correlations, while ignoring the underlying manifold structure across
different modalities, which is extremely helpful to capture meaningful nearest
neighbors of different modalities for cross-modal retrieval. To address the
above problem, in this paper we propose an Unsupervised Generative Adversarial
Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for
unsupervised representation learning to exploit the underlying manifold
structure of cross-modal data. The main contributions can be summarized as
follows: (1) We propose a generative adversarial network to model cross-modal
hashing in an unsupervised fashion. In the proposed UGACH, given a data of one
modality, the generative model tries to fit the distribution over the manifold
structure, and select informative data of another modality to challenge the
discriminative model. The discriminative model learns to distinguish the
generated data and the true positive data sampled from correlation graph to
achieve better retrieval accuracy. These two models are trained in an
adversarial way to improve each other and promote hashing function learning.
(2) We propose a correlation graph based approach to capture the underlying
manifold structure across different modalities, so that data of different
modalities but within the same manifold can have smaller Hamming distance and
promote retrieval accuracy. Extensive experiments compared with 6
state-of-the-art methods verify the effectiveness of our proposed approach.Comment: 8 pages, accepted by 32th AAAI Conference on Artificial Intelligence
(AAAI), 201
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
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