Unsupervised feature approach for content based image retrieval using principal component analysis

Memon, Muhammad Hammad; Shaikh, Riaz Ahmed; Li, Minglu; Khan, Asif; Memon, Imran; Deep, Samundra

doi:10.1109/iccwamtip.2014.7073406

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…Images contain a lot of geo-location information, such as the reference [6][7][8], they proposed different image retrieval methods to identify the geo-location of the images. First we use the Haversine [9] formula to calculate the distance between photos according to the latitude and longitude of each photo, when the distance between the photos are less than the given threshold, photos between them could be regarded as being taken at the same stop point, whose coordinate is represented by the average latitude and longitude of all the pictures taken by the user at this place.…”

Section: Prta-cf Algorithm the Generation Of The Attraction Setsmentioning

confidence: 99%

Personalized Recommendation of Tourist Attractions based on LBSN

Lv¹,

Li²,

Su³

et al. 2018

dtcse

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Photos metadata in Location-Based Social Networks (LBSN) contain rich time and space information, these metadata provide the basis for the research of personalized recommendation of tourist attractions. The existing methods have many problems such as low accuracy of recommendation and single type of attractions recommendation. For those problems, the PRTA-CF algorithm is proposed to improve the method of predicting the user's preference of attractions that he/she has not been to before, which is used in traditional collaborative filtering. We designed an evaluation model to assess the user's preference of the attractions he/she has been to. In order to predict the target user's preference to attractions he/she has not been to before, when we recommended to the target user, we took into account similar users' recommendation value and the popularity of attractions based on user preference. Experiment shows that compared with the traditional collaborative filtering algorithm and the algorithm only considered similar users' preference, it can effectively improve the accuracy of personalized recommendation of tourist attractions when considering both.

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Section: Prta-cf Algorithm the Generation Of The Attraction Setsmentioning

confidence: 99%

Personalized Recommendation of Tourist Attractions based on LBSN

Lv¹,

Li²,

Su³

et al. 2018

dtcse

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“…However they formulate the colorization-based compression problem into an optimization problem by constructing the colorization matrix in a multiscale manner. In [3], researcher propose a hybrid scheme that combines both the example-based colorization and the scribblebased colorization algorithms. Moreover they present a colorization method in YIQ color space which requires neither region tracking nor precise image segmentation.…”

mentioning

confidence: 99%

“…Methods [1,2] based on CBIR for texture image retrieval, might be different from nontexture image retrieval. Enhancement to the CBIR continue to ROI (Region of Interest) [3] based image retrieval. This approach provides better user interaction with the system.…”

mentioning

confidence: 99%

A Novel Technique for Region-Based Features Similarity for Content-Based Image Retrieval

Memon¹,

Arain²,

Pirzada³

2018

Mehran Univ. res. j. eng. technol.

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The study of feature extraction and clustering techniques for an image is carried out to find the future scope in the area of image clustering for the real world applications. Nowadays, a huge amount of image data is being collected in various application domains. As clustering approaches group homogeneous data together and also deals with unlabeled data, it is used quite often for accessing the interesting data Experimental results show that the retrieval performance of the proposed algorithm is better in comparison to other state-of-the-art methods.

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“…As the relative documents based on start-up companies and investment organizations disperse on different internet platforms and the documents have no normal management standard, the traditional supervised studying method cannot cope with these texts [8].…”

Section: Introductionmentioning

confidence: 99%

Heat Analysis of Entrepreneurial Hotspots

Li¹,

Kong²,

Lin³

et al. 2017

dtcse

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Abstract.Recently with the rapid development of entrepreneurship in China, many problems have been emerging such as overcrowding in the same entrepreneurship focuses and the investment bubbles. So, the key to be successful in entrepreneurship is to know the real hotspots in the market. However, there are little works related to the quantification analysis of entrepreneurial hotspots. This paper adopts Latent Dirichlet Allocation algorithm to analyze the data of 120,000 start-ups including their establishing time, financing stage, etc., and obtain the specific entrepreneurial hot topics of market. We also gave the rank list of these topics in different fields through a rule we designed. The statistical results in CB Insights database demonstrate that the analyzed results in hotspots basically accord with the real situation. Therefore, our proposed method for analyzing hotspots can provide valuable evaluation criteria to entrepreneurs and investors.

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Unsupervised feature approach for content based image retrieval using principal component analysis

Cited by 14 publications

References 8 publications

Personalized Recommendation of Tourist Attractions based on LBSN

Personalized Recommendation of Tourist Attractions based on LBSN

A Novel Technique for Region-Based Features Similarity for Content-Based Image Retrieval

Heat Analysis of Entrepreneurial Hotspots

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