{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:28:28Z","timestamp":1760243308825,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2014,7,14]],"date-time":"2014-07-14T00:00:00Z","timestamp":1405296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We propose a new geometric verification method in image retrieval\u2014Hierarchical Geometry Verification via Maximum Entropy Saliency (HGV)\u2014which aims at filtering the redundant matches and remaining the information of retrieval target in images which is partly out of the salient regions with hierarchical saliency and also fully exploring the geometric context of all visual words in images. First of all, we obtain hierarchical salient regions of a query image based on the maximum entropy principle and label visual features with salient tags. The tags added to the feature descriptors are used to compute the saliency matching score, and the scores are regarded as the weight information in the geometry verification step. Second we define a spatial pattern as a triangle composed of three matched features and evaluate the similarity between every two spatial patterns. Finally, we sum all spatial matching scores with weights to generate the final ranking list. Experiment results prove that Hierarchical Geometry Verification based on Maximum Entropy Saliency can not only improve retrieval accuracy, but also reduce the time consumption of the full retrieval.<\/jats:p>","DOI":"10.3390\/e16073848","type":"journal-article","created":{"date-parts":[[2014,7,14]],"date-time":"2014-07-14T11:33:53Z","timestamp":1405337633000},"page":"3848-3865","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hierarchical Geometry Verification via Maximum Entropy Saliency in Image Retrieval"],"prefix":"10.3390","volume":"16","author":[{"given":"Hongwei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingliang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingping","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Datta, R., Joshi, D., Li, J., and Wang, J.Z. (2008). Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv, 40.","DOI":"10.1145\/1348246.1348248"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bhandari, K., Dugar, N., Jain, N., and Shetty, N. (2010, January 26\u201327). A novel high performance multi-modal approach for content based image retrieval. Mumbai, Maharashtra, India.","DOI":"10.1145\/1741906.1741964"},{"key":"ref_3","unstructured":"Fiala, M. (2006, January 7\u20139). Using normalized interest point trajectories over scale for image search. Quebec City, QC, Canada."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Z.-H., Quan, Y., Li, W.-H., and Guo, W. (2006, January 13\u201316). A new content-based image retrieval. Dalian, China.","DOI":"10.1109\/ICMLC.2006.258801"},{"key":"ref_5","unstructured":"Yue, L., Wan, S., and Jin, P. (2009, January 11\u201312). An approach for image retrieval based on visual saliency. Taizhou, China."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.patcog.2006.04.045","article-title":"A survey of content-based image retrieval with high-level semantics","volume":"40","author":"Liu","year":"2007","journal-title":"Pattern Recogn"},{"key":"ref_7","unstructured":"Rutishauser, U., Walther, D., Koch, C., and Perona, P. (July, January 27). Is bottom-up attention useful for object recognition?. Washington, DC, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1016\/j.neunet.2006.10.001","article-title":"Modeling attention to salient proto-objects","volume":"19","author":"Walther","year":"2006","journal-title":"Neural Netw"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1023\/A:1012460413855","article-title":"Saliency, scale and image description","volume":"45","author":"Kadir","year":"2001","journal-title":"Int. J. Comput. Vis"},{"key":"ref_10","unstructured":"Soares, R.D.C., Silva, I.R.D., and Guliato, D. (2012, January 7\u20139). Spatial locality weighting of features using saliency map with a bag-of-visual-words approach. Athens, Greece."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sivic, J., and Zisserman, A. (2003, January 11\u201317). Video Google: A text retrieval approach to object matching in videos. Nice, France.","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chum, O., Perdoch, M., and Matas, J. (2009, January 20\u201325). Geometric min-hashing: Finding a (thick) needle in a haystack. Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206531"},{"key":"ref_13","unstructured":"Jegou, H., Douze, M., and Schmid, C. (2008). Computer Vision\u2013ECCV 2008, Springer."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A. (2007, January 18\u201323). Object retrieval with large vocabularies and fast spatial matching. Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383172"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, Z., Ke, Q., Isard, M., and Sun, J. (2009, January 20\u201325). Bundling features for large scale partial-duplicate web image search. Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206566"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TMM.2010.2050651","article-title":"On the annotation of web videos by efficient near-duplicate search","volume":"12","author":"Zhao","year":"2010","journal-title":"IEEE Trans. Multimed"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xie, H., Gao, K., Zhang, Y., and Li, J. (2011, January 11\u201314). Local geometric consistency constraint for image retrieval. Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6115596"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tsai, S.S., Chen, D., Takacs, G., Chandrasekhar, V., Vedantham, R., Grzeszczuk, R., and Girod, B. (2010, January 26\u201329). Fast geometric re-ranking for image-based retrieval. Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5648942"},{"key":"ref_21","unstructured":"Harel, J., Koch, C., and Perona, P. (2006, January 4\u20137). Graph-based visual saliency. Vancouver, BC, Canada."},{"key":"ref_22","unstructured":"Luo, G., Huang, W., and Li, S. (2010, January 22\u201324). 2-D maximum entropy spermatozoa image segmentation based on Canny operator. Guilin, China."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1016\/j.comnet.2008.02.015","article-title":"Decentralized detection of global threshold crossings using aggregation trees","volume":"52","author":"Wuhib","year":"2008","journal-title":"Comput. Netw"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.jvcir.2011.05.001","article-title":"Complex background modeling based on texture pattern flow with adaptive threshold propagation","volume":"22","author":"Zhang","year":"2011","journal-title":"J. Vis. Commun. Image Represent"},{"key":"ref_25","first-page":"159","article-title":"Determining the best threshold of rapid shallow breathing index in a therapist-implemented patient-specific weaning protocol","volume":"52","author":"Chao","year":"2007","journal-title":"Respir. Care"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","article-title":"A new method for gray-level picture thresholding using the entropy of the histogram","volume":"29","author":"Kapur","year":"1985","journal-title":"Comput. Vis. Graph. Image Process"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Milanese, R., Wechsler, H., Gill, S., Bost, J.-M., and Pun, T. (1994, January 21\u201323). Integration of bottom-up and top-down cues for visual attention using non-linear relaxation. Seattle, WA, USA.","DOI":"10.1109\/CVPR.1994.323898"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Maki, A., Nordlund, P., and Eklundh, J.-O. (1996, January 25\u201329). A computational model of depth-based attention. Vienna, Austria.","DOI":"10.1109\/ICPR.1996.547661"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gopalakrishnan, V., Hu, Y., and Rajan, D. (2009, January 20\u201325). Random walks on graphs to model saliency in images. Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206767"},{"key":"ref_31","unstructured":"Costa, L.D.F. (2006). Visual saliency and attention as random walks on complex networks. arXiv preprint physics\/0603025."},{"key":"ref_32","unstructured":"DupImage. Available online: https:\/\/dl.dropboxusercontent.com\/u\/42311725\/DupGroundTruthDataset.rar."},{"key":"ref_33","unstructured":"Flickr. Available online: http:\/\/www.flickr.com\/."},{"key":"ref_34","unstructured":"Image.vary.jpg. Available online: http:\/\/www.db.stanford.edu\/~wangz\/image.vary.jpg.tar."},{"key":"ref_35","unstructured":"Nister, D., and Stewenius, H. (2006, January 17\u201322). Scalable recognition with a vocabulary tree. New York, NY, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A. (2007, January 17\u201322). Object retrieval with large vocabularies and fast spatial matching. 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