NDLI: Latent semantic sparse hashing for cross-modal similarity search

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Putting searchers into search

Modelling interaction with economic models of search

Temporal feedback for tweet search with non-parametric density estimation

Gaussian process factorization machines for context-aware recommendations

Context-aware web search abandonment prediction

Circumlocution in diagnostic medical queries

Supervised hashing with latent factor models

Hierarchical multi-label classification of social text streams

Predictive parallelization: taming tail latencies in web search

Learning for search result diversification

Seeking simplicity in search user interfaces

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Weighted aspect-based collaborative filtering

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Second order probabilistic models for within-document novelty detection in academic articles

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Exploiting Twitter and Wikipedia for the annotation of event images

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Predicting query performance in microblog retrieval

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Reputation analysis with a ranked sentiment-lexicon

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Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream

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Dynamic location models

Wikipedia-based query performance prediction

A revisit to social network-based recommender systems

Latent semantic sparse hashing for cross-modal similarity search

Content Provider	ACM Digital Library
Author	Ding, Guiguang Guo, Yuchen Zhou, Jile
Abstract	Similarity search methods based on hashing for effective and efficient cross-modal retrieval on large-scale multimedia databases with massive text and images have attracted considerable attention. The core problem of cross-modal hashing is how to effectively construct correlation between multi-modal representations which are heterogeneous intrinsically in the process of hash function learning. Analogous to Canonical Correlation Analysis (CCA), most existing cross-modal hash methods embed the heterogeneous data into a joint abstraction space by linear projections. However, these methods fail to bridge the semantic gap more effectively, and capture high-level latent semantic information which has been proved that it can lead to better performance for image retrieval. To address these challenges, in this paper, we propose a novel Latent Semantic Sparse Hashing (LSSH) to perform cross-modal similarity search by employing Sparse Coding and Matrix Factorization. In particular, LSSH uses Sparse Coding to capture the salient structures of images, and Matrix Factorization to learn the latent concepts from text. Then the learned latent semantic features are mapped to a joint abstraction space. Moreover, an iterative strategy is applied to derive optimal solutions efficiently, and it helps LSSH to explore the correlation between multi-modal representations efficiently and automatically. Finally, the unified hashcodes are generated through the high level abstraction space by quantization. Extensive experiments on three different datasets highlight the advantage of our method under cross-modal scenarios and show that LSSH significantly outperforms several state-of-the-art methods.
Starting Page	415
Ending Page	424
Page Count	10
File Format	PDF
ISBN	9781450322577
DOI	10.1145/2600428.2609610
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Correlation Cross-modal retrieval Hashing Matrix factorization Sparse coding Heterogeneous data sources
Content Type	Text
Resource Type	Article

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