Abstract
In recent years, online shopping has grown exponentially and huge number of images are available online. Hence, it is necessary to recommend various product images to aid the user in effortless and efficient access to the desired products. In this paper, we present image recommendation framework with user relevance feedback session and visual features (IR_URFS_VF) to extract relevant images based on user inputs. User feedback is retrieved from image search history with clicked and un-clicked images. Image features are computed off-line and later used to find relevance between images. The relevance between images is determined by cosine similarity and are ranked based on clicked frequency and similarity score between images. Experiments results show that IR_URFS_VF outperforms CBIR method by providing more relevant ranked images to the user input query.
Similar content being viewed by others
References
Bin X, Jiajun B, Chen C, Wang C, Cai D, He X (2015) EMR: a scalable graph-based ranking model for content-based image retrieval. IEEE Trans Knowl Data Eng 27(1):102–114
Fan J (2009) Daniel AK, Yuli G, Hangzai L, Zongmin Li. JustClick: personalized image recommendation via exploratory search from large-scale flickr images. IEEE Transactions on Circuits and Systems for Video Technology 19(2):273–288
Broilo M, De Natale FGB (2010) A stochastic approach to image retrieval using relevance feedback and particle swarm optimization. IEEE Trans Multimedia 12(4):267–277
Qin T, Zhang X-D, Liu T-Y, Wang D-S, Ma W-Y, Zhang H-J (2008) An active feedback framework for image retrieval. Pattern Recognit Lett 29(5):637–646
Salton G, Buckley C (1997) Improving retrieval performance by relevance feedback. Read Inf Retr 24(5):355–363
Yin P-Y, Bhanu B, Chang K-C, Dong A (2005) Integrating relevance feedback techniques for image retrieval using reinforcement learning. IEEE Trans Pattern Anal Mach Intell 27(10):1536–1551
Demir B, Bruzzone L (2015) A novel active learning method in relevance feedback for content-based remote sensing image retrieval. IEEE Trans Geosci Rem Sens 53(5):2323–2334
Georgios TP, Konstantinos CA, Petros D (2014) Gaze-based relevance feedback for realizing region-based image retrieval. IEEE Transactions on Multimedia 16(2):440–454
Chen Y, Wang JZ, Krovetz R (2005) CLUE: cluster-based retrieval of images by unsupervised learning. IEEE Trans Image Process 14(8):1187–1201
Li X, Shou L, Chen G, Tianlei H, Dong J (2008) Modeling image data for effective indexing and retrieval in large general image databases. IEEE Trans Knowl Data Eng 20(11):1566–1580
Ramachandra A, Abhilash S, Raja KB, Venugopal KR (2012) Feature level fusion based bimodal biometric using transformation domine techniques. IOSR J Comput Eng (IOSRJCE) 3(3):39–46
Lavanya BN, Raja KB, Venugopal KR, Patnaik LM (2009) Minutiae Extraction in Fingerprint using Gabor Filter Enhancement. International Conference on Advances in Computing, Control, & Telecommunication Technologies, pp 54–56
Yin P-Y, Bhanu B, Chang K-C, Dong A (2008) Long-term cross-session relevance feedback using virtual features. IEEE Trans Knowl Data Eng 20(3):352–368
Ja-Hwung S, Huang W-J, Yu PS, Tseng VS (2011) Efficient relevance feedback for content-based image retrieval by mining user navigation patterns. IEEE Trans Knowl Data Eng 23(3):360–372
Rahman MM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback. IEEE Trans Inf Technol Biomed 15(4):640–646
Tang X, Liu K, Cui J, Wen F, Wang X (2012) IntentSearch: capturing user intention for one-click internet image search. IEEE Trans Pattern Anal Mach Intell 34(7):1342–1353
Jones R, Klinkner, Kristina L (2008) Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In: The Proceedings of the 17th ACM conference on Information and Knowledge Management, pp 699–708
Huang J, Kumar SR, Mitra M, Zhu W-J, Zabih R (1997) Image indexing using color correlograms. In: The Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 762–768
Zha Z-J, Yang L, Mei T, Wang M, Wang Z, Chua T-S, Hua X-S (2010) Visual query suggestion: towards capturing user intent in internet image search. ACM Trans Multimedia Comput Commun Appl (TOMM) 6(3):13
Cai D, He X, Li Z, Ma W-Y, Wen J-R (2004) Hierarchical clustering of WWW image search results using visual, textual and link information. In: The Proceedings of the 12th Annual ACM International Conference on Multimedia, pp 952–959
Lu Z, Yang X, Lin W, Chen X, Zha H (2011) Inferring users’ image-search goals with pseudo-images. In: The Proceedings of IEEE Conference on Visual Communications and Image Processing (VCIP), pp 1–4
Eakins J, Graham M (1999) Content-based image retrieval. University of Northumbria at Newcastle
Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380
Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimedia Comput Commun Appl (TOMM) 2(1):1–19
Datta R, Joshi D, Li J, Wang JZ (2008) Image Retrieval: Ideas, Influences, and Trends of the New Age. Journal on ACM Computing Surveys (CSUR) 40(2):5
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sejal, D., Abhishek, D., Venugopal, K.R. et al. IR_URFS_VF: image recommendation with user relevance feedback session and visual features in vertical image search. Int J Multimed Info Retr 5, 255–264 (2016). https://doi.org/10.1007/s13735-016-0111-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13735-016-0111-x