Image Search Engine and Individual Profile Building
Keywords:
World Wide Web, Search Engine, WikipediaAbstract
Look technique utilized for the content gives semantically significant outcome, however isn't a similar with regards to the scan strategy utilized for pictures. Interactive media information is being distributed on the Web at an extraordinary rate. Likewise, in this time of innovation, it is conceivable to get data about any person from web. It has turned out to be fundamental to perform picture hunt of a person to recover the comparative pictures from Web. It is even conceivable to get any kind of data about any superstar from Wikipedia and different locales. This project aims at building the Image Search Engine for recovering the pictures just as structure the profile of a person, from World Wide Web. This is finished via preparing set of pictures of an individual and after that the web crawler creeps over the connections for getting the pertinent pictures. These recovered pictures coordinate with the name entered by the client. A similar outcome is utilized to get the data and manufacture the profile of a similar individual by slithering over the connections.
Downloads
References
Li Cuimei, Qi Zhilliang, Jia Nan, Wu Jianhua, “Human face detection algorithm via Haar cascade classifier combined with three additional classifiers,” 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2017.
Surbhi Jain, Joydip Dhar, “Image Based Search Engine Using Deep Learning,” Proceedings of 2017 Tenth International Conference on Contemporary Computing (IC3), 10-12 August 2017, Noida, India.
A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, “Neural codes for image retrieval,” in European conference on computer vision. Springer, 2017, pp. 584– 599.
R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan, “Supervised hashing for image retrieval via image representation learning,” in the 1st International workshop on Edge Systems, Analytics and Networking, vol. 1, 2017, p. 2.10.1145/3213344.3212248/Vol 25 June 2017.
K. Lin, H.-F. Yang, J.-H. Hsiao, and C.-S. Chen, “Deep learning of binary hash codes for fast image retrieval,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 27–35.
N. Khosla and V. Venkataraman, “Building image-based shoe search using convolutional neural networks,” CS231n Course Project Reports, 2017.
Dhananjay Uttarwar, Aakash Agarwal, Riyaz Kadiwar and Vijay D. Katkar, “Distributed Content Based Image Search Engine using Hadoop Framework”, International Conference on Communication and Signal Processing, April 6-8, 2017, India.
Lu Zhang, Zhan Bu, Zhiang Wu and Jie Cao,” DGWC: Distributed and Generic Web Crawler for Online Information Extraction”, 2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), Vol. 0, No. 0, Article 00, Publication date: 2017 DOI: 10.1145/3057266.
Wichian Premchaiswadi, Anucha Tungkatsathan, and SarayutIntarasema, “Improving Performance of Content Based ImageRetrieval Schemes using Hadoop MapReduce”, 2013 IEEE Biomedical Engineering, vol. 57, 2016.
A.Bhagyalakshmi and V.VijayaChamundeeswari, “Image retrieval using Color and Texture Binary Patterns”, 2016 International Conference on Green Computing and Internet of Things (ICGCIoT).
Akash K Sabarad, Mohamed HumairKankudti, Meena S M, and Moula Husain, “Color and Texture Feature Extraction using Apache Hadoop Framework”, 2016 International Conference on Computing Communication Control and Automation.
David Edmundson and Gerald Schaefer, M. Emre Celebi, “Similarity-based Browsing of Image Search Results”, 2016 IEEE International Symposium on Multimedia, Vol 7 (2.7) (2018) 335-340.
R. Jenke, A. Peer and M. Buss, "Feature Extraction and Selection for Emotion Recognition from EEG," IEEE Transactions on Affective Computing, vol. 5, pp. 327- 339, 2016.
W. Plant and G. Schaefer, “Visualisation and browsing of image databases,” in Multimedia Analysis, Processing and Communications, ser. Studies in Computational Intelligence. Springer, 2011, vol. 346, pp. 3–57M. Robnik- Š ikonja and I. Kononenko, "Theoretical and
Empirical Analysis of ReliefF and RReliefF," Machine Learning, vol. 53, pp. 23-69, 2016.