Multi-Query Parallel Field Ranking for image retrieval

Relevance feedback image retrieval is an effective scheme bridging the gap between low-level features and high-level concepts. It is essentially a multi-query ranking problem where the user submitted image and provided positive examples are considered as queries. Most of the existing approaches either merge the multiple queries into a single query or consider them independently, and then the geodesic distances on the image manifold are used to measure the similarities between the query image and the other images in database. In this paper, we propose a novel approach called Multi-Query Parallel Field Ranking (MQPFR) which finds an optimal ranking function whose gradient field is as parallel as possible. In this way, the obtained ranking function varies linearly along the geodesics of the data manifold, and achieves the highest value at the multiple queries simultaneously. Extensive experiments are carried out on a large image database and demonstrate the effectiveness of the proposed approach.

[1]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[2]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[3]  Wei-Ying Ma,et al.  Learning an image manifold for retrieval , 2004, MULTIMEDIA '04.

[4]  Philip S. Yu,et al.  Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns , 2011, IEEE Transactions on Knowledge and Data Engineering.

[5]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[6]  Yi Yang,et al.  A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Qi Tian,et al.  Learning image manifolds by semantic subspace projection , 2006, MM '06.

[8]  Bernhard Schölkopf,et al.  Ranking on Data Manifolds , 2003, NIPS.

[9]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[10]  I. Holopainen.  Riemannian Geometry , 1927, Nature.

[11]  Xiaofei He,et al.  Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval , 2010, IEEE Transactions on Image Processing.

[12]  Yihong Gong,et al.  Nonlinear Learning using Local Coordinate Coding , 2009, NIPS.

[13]  Bin Wang,et al.  Manifold-ranking based retrieval using k-regular nearest neighbor graph , 2012, Pattern Recognit..

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Yi Yang,et al.  Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval , 2008, IEEE Transactions on Multimedia.

[16]  G. Chowdhury,et al.  Introduction to Modern Information Retrieval, 3rd Edition , 2010 .

[17]  Jiawei Han,et al.  Parallel Field Ranking , 2013, ACM Trans. Knowl. Discov. Data.

[18]  Heng Huang,et al.  Ball Ranking Machine for Content-Based Multimedia Retrieval , 2011, IJCAI.

[19]  Bo Zhang,et al.  Support vector machine learning for image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[20]  Samy Bengio,et al.  Large Scale Online Learning of Image Similarity through Ranking , 2009, IbPRIA.

[21]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[22]  Jingrui He,et al.  Manifold-ranking based image retrieval , 2004, MULTIMEDIA '04.

[23]  Gene H. Golub,et al.  Matrix computations , 1983 .

[24]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[25]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[27]  Chun Chen,et al.  Efficient manifold ranking for image retrieval , 2011, SIGIR.

[28]  Xiaofei He,et al.  Semi-supervised Regression via Parallel Field Regularization , 2011, NIPS.

[29]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Wei Liu,et al.  Large Graph Construction for Scalable Semi-Supervised Learning , 2010, ICML.

[31]  Jiawei Han,et al.  Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[32]  Mikhail Belkin,et al.  An iterated graph laplacian approach for ranking on manifolds , 2011, KDD.

[33]  Yi Yang,et al.  Harmonizing Hierarchical Manifolds for Multimedia Document Semantics Understanding and Cross-Media Retrieval , 2008, IEEE Transactions on Multimedia.

[34]  Zhuowen Tu,et al.  Learning Context-Sensitive Shape Similarity by Graph Transduction , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[36]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[37]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..