Abstract
With the enormous growth in the number of images on the web, image clustering has become an essential part of any image retrieval system. Since web images are often accompanied by related text or tags, both visual and textual features can be exploited to improve the precision of web image clustering. Existing clustering methods either utilize them separately in a specific order, or use them simultaneously, but independently. In this work, we propose a new framework, Multimodal Hierarchical Clustering for Images (MHCI), which exploits the coexistence of both visual and textual patterns to establish a relationship between them. We propose textual and visual weights to quantify the relationship established between images and their features. The proposed framework can be applied to a wide variety of image datasets with different characteristics, viz., search results with noisy surrounding text, and tagged images. It can also cluster image search queries and their corresponding clicked images. The respective datasets used include image search results, Flicker (NUS-WIDE), and Clickture (Bing query-log). The proposed framework is shown to be versatile on Clickture dataset, which has not been examined by any of the previous approaches. The experimental results show that MHCI significantly improves the quality of image clusters as compared to existing methods.
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M. A. Abebe, J. Tekli, F. Getahun, G. Tekli, and R. Chbeir (2016) A General Multimedia Representation Space Model toward Event-Based Collective Knowledge Management, In: Proc. CSE/ EUC/ DCABES , pp. 512–521
Agrawal R, Wu C, Grosky WI, Fotouhi F (2007) Image clustering using visual and text keywords. Symposium CIRA-IEEE, Jacksonville, pp 49–54
An J, Chen YPP, Chen H DDR: An Index Method for Large Time Series Datasets. Inf Syst 30(5):333–348
I. Ayoub, K. J. Codoumi, and J Tekli (2016) Personalized Social Image Organization, Visualization, and Querying Tool Using Low- and High-Level Features, In: Proc. CSE/ EUC/ DCABES, pp. 287–294
Beeferman D, Berger A (2000) Agglomerative clustering of a search engine query log. In: Proc. SIGKDD-ACM, pp. 407–416
Broilo M (2010) A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization. IEEE Trans TMM 12(4):267–277
Cai D, He X, Li Z, Ma W, Wen J (2004) Hierarchical clustering of WWW image search results using visual, textual and link information. Proc. Multimedia-ACM, New York, pp 10–16
J. Chang, L. Wang, G. Meng, S. Xiang, and C. Pan (2017) Deep Adaptive Image Clustering, In: Proc. ICCV
Chen Y, Dong M, Wan W (2009) Image co-clustering with multi-modality features and user feedbacks. Proc. Multimedia-ACM, New York, pp 689–692
Chen Y, Wang JZ, Krovetz R (2005) CLUE: cluster-based retrieval of images by unsupervised learning. IEEE Trans Image Processing 14(8):1187–1201
Chua TS, Tang J, Hong R, Li H, Luo Z, Zheng YT (2009) Nus-wide: A real-world Web image database from national university of Singapore. In: Proc. CIVR-ACM
Cutting DR, Karger DR, Pedersen JO, Tukey JW (1992) Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections. In: SIGIR, pp. 318–329
Dubes RC, Jain AK (1988) Algorithms for clustering data. Prentice Hall, Upper Saddle River, NJ, USA
Gao B, Liu T, Qin T, Zhenget X, Cheng Q, Ma W (2005) Web image clustering by consistent utilization of visual features and surrounding texts. Proc. Multimedia-ACM, New York, pp 112–121
Goyal P, Mehala N (2011) Concept based query recommendation. Proc. AusDM, Ballarat
Hamzaoui A, Joly A, Boujemaa N (2011) Multi-source shared nearest neighbours for multi-modal image clustering. MTAP Springer US 51(2):479–503
Hoi SC, Liu W, Chang S (2008) Semi-supervised distance metric learning for collaborative image retrieval. In: Proc. CVPR-IEEE, pp. 1–7
Hu Y, Yu N, Li Z, Li M (2007) Image search result clustering and re-ranking via partial grouping. Proc. ICME-IEEE, Beijing, pp 603–606
Hua XS et al (2013) Clickture: A large-scale real-world image dataset. In: Microsoft Research Technical Report MSR-TR-2013-75
Jing F, Wang C, Yao Y, Deng K, Zhang L, Ma WC (2006) IGroup: Web image search results clustering. Proc. Multimedia-ACM, New York, pp 587–596
Kobayashi M, Kameyama K (2008) User-Adaptive Image Clustering using Relevance Feedback for Efficient Content-Based Retrieval. In: Proc. IEEE SMC
Krischnamachari S, Abdel-Mottaleb M (1999) Image browsing using hierarchical clustering. In: IEEE symposium on computers and communications, pp. 301–307
Larsen B, Aone C (1999) Fast and Effective Text Mining Using Linear-time Document Clusterin. In: KDD, California
Lee KM (2010) Cluster-Driven Refinement for Content-Based Digital Image Retrieval. IEEE Trans TMM 12(6):817–827
Leuken RHV, Garcia L, Olivares X, Zwol R (2009) Visual diversification of image search results. Proc WWW-ACM, New York, pp 341–350
Li X, Cui G, Dong Y (2016) Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering. IEEE Trans Cybernetics 99:1–14
Li H, He X, Tao D, Tang Y, Wang R (2018) Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning. Pattern Recogn 79:130–146
Li P, Wang M, Cheng J, Xu C, Lu H (2013) Spectral Hashing With Semantically Consistent Graph for Image Indexing. IEEE Trans TMM 15(1):141–152
Liang J, Han Y, Hu Q (2016) Semi-Supervised image clustering with multi-modal information. ACM Multimedia System 22(2):149–160
Liu Q, Sun Y, Wang C, Liu T, Tao D (2017) Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification. IEEE Trans Image Processing 26(1):452–463
Lowe DG (1999) Object recognition from local scale-invariant features. Proc. Computer Vision-IEEE, Kerkyra, pp 1150–1157
Ma H, Zhu J, Lyu MRT, King I (2010) Bridging the Semantic Gap Between Image Contents and Tags. IEEE Trans TMM 12(5):462–473
Moëllic PA, Haugeard J, Pitel G (2008) Image clustering based on a shared nearest neighbors approach for tagged collections. Proc. CIVR-ACM, New York, pp 269–278
Nahar J, Imam T, Tickle K, Chen YPP (2013) Computational Intelligence for Heart Disease Diagnosis: A Medical Knowledge Driven Approach. Expert Syst Appl 40(1):96–104
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans TPAMI 24(7):971–987
Pedronette DCG, Torres RDS (2012) Exploiting pairwise recommendation and clustering strategies for image re-ranking. Inf Sci 207:19–34
Picsearch image search. http://www.picsearch.com Accessed: May 2015
Priyogi B, Selviandro N, Hasibuan ZA, Ahmad M (2014) Image Clustering Using Multi-visual Features. Lecture Notes in Computer Science Information and Communication Technology 8407:179–189
Rege M, Dong M, Hua J (2008) Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering. Proc. WWW-ACM, New York, pp 317–326
Smith JR (2002) Color for image retrieval. In: Image Databases, John Wiley & Sons, Inc., 11, pp. 285–311
Tan P-N, Steinbach M, Kumar V (2014) Introduction to Data Mining
Tang X, Liu K, Cui J, Wen F, Wang X (2012) Intentsearch: Capturing user intention for one-click internet image search. IEEE Trans TPAMI 34(7):1342–1353
Tao D, Cheng J, Yu Z, Yue K, Wang L (2018) Domain-Weighted Majority Voting for Crowdsourcing. IEEE trans Neural Networks and Learning Systems, pp. 1–12
Tao D, Guo Y, Li Y, Gao X (2018) Tensor Rank Preserving Discriminant Analysis for Facial Recognition. IEEE Trans Image Processing 27:325–334
Tsai JT, Lin YY, Liao HYM (2014) Per-Cluster Ensemble Kernel Learning for Multi-Modal Image Clustering With Group-Dependent Feature Selection. IEEE Trans TMM 16(8):2229–2241
Wang XD, Chen RC, Hong CQ, Zeng ZQ, Zhou ZL (2017) Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding. Image Vis Comput 63:10–23
Wang XD, Chen RC, Zeng ZQ, Hong CQ, Yan F (2018) Robust Dimension Reduction for Clustering With Local Adaptive Learning. IEEE trans Neural Network Learning Systems
Wang X, Zhang X, Zeng Z, Wu Q, Zhang J (2016) Unsupervised spectral feature selection with l1-norm graph. Neurocomputing 200:47–54
Wu F, Pai HT, Yan YF, Chuang J (2014) Clustering results of image searches by annotations and visual features. Telematics Inform 31(3):477–491
Xia DS, Xiang ZQ, Zou YX (2015) Integrating visual and textual features for web image clustering, vol 2015. Proc. BigMM-IEEE, Beijing, pp 116–123
Yan Y, Liu G, Wang S, Zhang J, Zheng K (2017) Graph-based clustering and ranking for diversified image search. ACM Multimedia Systems 23(1):41–52
Yang Y, Yang L, Wu G, Li S (2014) Image Relevance Prediction Using Query-Context Bag-of-Object Retrieval Model. IEEE Trans TMM 16(6):1700–1712
Yu J, Rui Y, Chen B (2014) Exploiting Click Constraints and Multi-view Features for Image Re-ranking. IEEE Trans TMM 16(1):159–168
Zhao R (2002) Narrowing the Semantic Gap—Improved Text-Based Web Document Retrieval Using Visual Features. IEEE Trans TMM 4(2):189–200
Zhao K, Cai Z, Sui Q, Wei E, Zh KQ (2014) Clustering image search results by entity disambiguation. Lecture Notes in Computer Science Machine Learning and Knowledge Discovery in Databases 8726:369–384
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Chaudhary, C., Goyal, P., Tuli, S. et al. A novel multimodal clustering framework for images with diverse associated text. Multimed Tools Appl 78, 17623–17652 (2019). https://doi.org/10.1007/s11042-018-7131-x
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DOI: https://doi.org/10.1007/s11042-018-7131-x