2013-2014 > Academic research: refereed > Publication in refereed journa

Beis J. S.

Deng J.

Jegou H.

Liu T.

Muja M.

Philbin J.

Shen X.

Silpa-Anan C.

Sivic J.

Bag-of-visual-words (BOVW)-based image representation has received intense attention in recent years and has improved content-based image retrieval (CBIR) significantly. BOVW does not consider the spatial correlation between visual words in natural images and thus biases the generated visual words toward noise when the corresponding visual features are not stable. This article outlines the construction of a visual word co-occurrence matrix by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. Based on this co-occurrence matrix, we first present a novel high-order predictor to accelerate the generation of spatially correlated visual words and a penalty tree (PTree) to continue generating the words after the prediction. Subsequently, we propose two methods of co-occurrence weighting similarity measure for image ranking: Co-Cosine and Co-TFIDF. These two new schemes downweight the contributions of the words that are less discriminative because of frequent co-occurrences with other words. We conduct experiments on Oxford and Paris Building datasets, in which the ImageNet dataset is used to implement a large-scale evaluation. Cross-dataset evaluations between the Oxford and Paris datasets and Oxford and Holidays datasets are also provided. Thorough experimental results suggest that our method outperforms the state of the art without adding much additional cost to the BOVW model

Shi, Miaojing

Sun, Xinghai

Tao, Dacheng

Xu, Chao

Baciu, George

Liu, Hong

Edinburgh Research Explorer

Exploring Spatial Correlation for Visual Object Retrieval

Bag-of-visual-words (BOVW) based image representation has received intense attention in recent years and has improved content based image retrieval (CBIR) significantly. BOVW does not consider the spatial correlation between visual words in natural images, and thus, biases the generated visual words towards noise when the corresponding visual features are not stable. This paper outlines the construction of a visual word co-occurrence matrix by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. Based on this co-occurrence matrix, we first present a novel high-order predictor to accelerate the generation of spatially correlated visual words, and a penalty tree (PTree) to continue generating the words after the prediction. Subsequently, we propose two methods of co-occurrence weighting similarity measure for image ranking: Co-Cosine and Co-TFIDF. These two new schemes down-weight the contributions of the words that are less discriminative because of frequent cooccurrences with other words. We conduct experiments on Oxford and Paris Building datasets, in which the ImageNet dataset is used to implement a large scale evaluation. Cross dataset evaluations between Oxford and Paris datasets, Oxford and Holidays datasets are also provided. Thorough experimental results suggest that our method outperforms the state-of-the-art without adding much additional cost to the BOVW model.Department of Computin

Shi, M

Sun, X

Tao, D

Xu, C

George, B

Liu, H

The Hong Kong Polytechnic University Pao Yue-kong Library

ACM Transactions on Intelligent Systems and Technology

Exploring spatial correlation for visual object retrieval

Bag-of-visual-words (BOVW)-based image representation has received intense attention in recent years and has improved content-based image retrieval (CBIR) significantly. BOVW does not consider the spatial correlation between visual words in natural images and thus biases the generated visual words toward noise when the corresponding visual features are not stable. This article outlines the construction of a visual word co-occurrence matrix by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. Based on this co-occurrence matrix, we first present a novel high-order predictor to accelerate the generation of spatially correlated visual words and a penalty tree (PTree) to continue generating the words after the prediction. Subsequently, we propose two methods of co-occurrence weighting similarity measure for image ranking: Co-Cosine and Co-TFIDF. These two new schemes downweight the contributions of the words that are less discriminative because of frequent co-occurrences with other words.We conduct experiments on Oxford and Paris Building datasets, in which the ImageNet dataset is used to implement a large-scale evaluation. Cross-dataset evaluations between the Oxford and Paris datasets and Oxford and Holidays datasets are also provided. Thorough experimental results suggest that our method outperforms the state of the art without adding much additional cost to the BOVW model. ? 2015 ACM.SCI(E)EI0ARTICLEshimj@cis.pku.edu.cn; sunxh@cis.pku.edu.cn; dacheng.tao@uts.edu.au;   xuchao@cis.pku.edu.cn; csgeorge@comp.polyu.edu.hk;   liuhong@cis.pku.edu.cn2

Institutional Repository of Peking University


            Bag-of-visual-words (BOVW)-based image representation has received intense attention in recent years and has improved content-based image retrieval (CBIR) significantly. BOVW does not consider the spatial correlation between visual words in natural images and thus biases the generated visual words toward noise when the corresponding visual features are not stable. This article outlines the construction of a visual word co-occurrence matrix by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. Based on this co-occurrence matrix, we first present a novel high-order predictor to accelerate the generation of spatially correlated visual words and a penalty tree (PTree) to continue generating the words after the prediction. Subsequently, we propose two methods of co-occurrence weighting similarity measure for image ranking: Co-Cosine and Co-TFIDF. These two new schemes down-weight the contributions of the words that are less discriminative because of frequent co-occurrences with other words. We conduct experiments on
            Oxford
            and
            Paris Building
            datasets, in which the
            ImageNet
            dataset is used to implement a large-scale evaluation. Cross-dataset evaluations between the
            Oxford
            and
            Paris
            datasets and
            Oxford
            and
            Holidays
            datasets are also provided. Thorough experimental results suggest that our method outperforms the state of the art without adding much additional cost to the BOVW model.
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Exploring Spatial Correlation for Visual Object Retrieval

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Institutional Repository of Peking University

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