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Similarity Search in High Dimensions via Hashing
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Video Google: A Text Retrieval Approach to Object Matching in Videos
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Robust Real-Time Face Detection
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Distinctive Image Features from Scale-Invariant Keypoints
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Creating Efficient Codebooks for Visual Recognition
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Object Categorization by Learned Universal Visual Dictionary
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Scalable Recognition with a Vocabulary Tree
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Spatial Weighting for Bag-of-Features
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Discriminative Object Class Models of Appearance and Shape by Correlatons
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Video search re-ranking via multi-graph propagation
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Universal and Adapted Vocabularies for Generic Visual Categorization
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Randomized Clustering Forests for Image Classification
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VisualRank: Applying PageRank to Large-Scale Image Search
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80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
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Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment
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Bayesian video search reranking
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SIFT-Bag kernel for video event analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Video event detection using motion relativity and visual relatedness
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Supervised Learning of Quantizer Codebooks by Information Loss Minimization
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Visual block link analysis for image re-ranking
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Latent visual context analysis for image re-ranking
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MI-SIFT: mirror and inversion invariant generalization for SIFT descriptor
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Boosting image object retrieval and indexing by automatically discovered pseudo-objects
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Discriminative codeword selection for image representation
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The third eye: mining the visual cognition across multi-language communities
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Landmark image retrieval using visual synonyms
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Personalization in multimedia retrieval: A survey
Multimedia Tools and Applications
Building descriptive and discriminative visual codebook for large-scale image applications
Multimedia Tools and Applications
Modeling spatial and semantic cues for large-scale near-duplicated image retrieval
Computer Vision and Image Understanding
A BOVW based query generative model
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Video retrieval based on words-of-interest selection
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Latent visual context learning for web image applications
Pattern Recognition
Words-of-interest selection based on temporal motion coherence for video retrieval
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Tagging image by exploring weighted correlation between visual features and tags
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Content based image retrieval using visual-words distribution entropy
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Large scale image search with geometric coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Spatial pooling for transformation invariant image representation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Contextual synonym dictionary for visual object retrieval
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Visual synonyms for landmark image retrieval
Computer Vision and Image Understanding
Visual vocabulary optimization with spatial context for image annotation and classification
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High-confidence near-duplicate image detection
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Visual pattern discovery for architecture image classification and product image search
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Cross community news event summary generation based on collaborative ranking
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Tag ranking by propagating relevance over tag and image graphs
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A bag-of-objects retrieval model for web image search
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Exploiting visual word co-occurrence for image retrieval
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Query-driven iterated neighborhood graph search for large scale indexing
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Embedding spatial context information into inverted filefor large-scale image retrieval
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Spatial pooling of heterogeneous features for image applications
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Image tag re-ranking by coupled probability transition
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Visual query attributes suggestion
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Query expansion enhancement by fast binary matching
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Topic based pose relevance learning in dance archives
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Information Processing and Management: an International Journal
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SIFT match verification by geometric coding for large-scale partial-duplicate web image search
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Learning attribute-aware dictionary for image classification and search
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Visual object analysis using regions and interest points
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Improved binary feature matching through fusion of hamming distance and fragile bit weight
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Spatial weighting for bag-of-features based image retrieval
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
Discriminative two-level feature selection for realistic human action recognition
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Spatiotemporal bag-of-features for early wildfire smoke detection
Image and Vision Computing
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Computer Vision and Image Understanding
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The Bag-of-visual Words (BoW) image representation has been applied for various problems in the fields of multimedia and computer vision. The basic idea is to represent images as visual documents composed of repeatable and distinctive visual elements, which are comparable to the words in texts. However, massive experiments show that the commonly used visual words are not as expressive as the text words, which is not desirable because it hinders their effectiveness in various applications. In this paper, Descriptive Visual Words (DVWs) and Descriptive Visual Phrases (DVPs) are proposed as the visual correspondences to text words and phrases, where visual phrases refer to the frequently co-occurring visual word pairs. Since images are the carriers of visual objects and scenes, novel descriptive visual element set can be composed by the visual words and their combinations which are effective in representing certain visual objects or scenes. Based on this idea, a general framework is proposed for generating DVWs and DVPs from classic visual words for various applications. In a large-scale image database containing 1506 object and scene categories, the visual words and visual word pairs descriptive to certain scenes or objects are identified as the DVWs and DVPs. Experiments show that the DVWs and DVPs are compact and descriptive, thus are more comparable with the text words than the classic visual words. We apply the identified DVWs and DVPs in several applications including image retrieval, image re-ranking, and object recognition. The DVW and DVP combination outperforms the classic visual words by 19.5% and 80% in image retrieval and object recognition tasks, respectively. The DVW and DVP based image re-ranking algorithm: DWPRank outperforms the state-of-the-art VisualRank by 12.4% in accuracy and about 11 times faster in efficiency.