A relevance feedback CBIR algorithm based on fuzzy sets

Arevalillo‐Herráez, Miguel; Zacarés, Mario; Benavent, Xaro; Ves, Esther de

doi:10.1016/j.image.2008.04.016

Cited by 18 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 15 illustrates the P × t curves for the 6 visual descriptors considered, while Fig. 16 illustrates the P × t curves for 15 Relevance feedback: evolution of P@20 measure for each iteration considering visual descriptors on the UW Dataset [49] the textual descriptors. We can observe an increasing precision score for descriptors of both modalities along iterations.…”

Section: Relevance Feedback Resultsmentioning

confidence: 99%

“…A fuzzy approach [15] was also used for modeling relevance feedback tasks. A fuzzy set is defined, so that the degree of membership of each image to this fuzzy set is related to the user's interest in that image [15].…”

Section: Related Workmentioning

confidence: 99%

“…A fuzzy set is defined, so that the degree of membership of each image to this fuzzy set is related to the user's interest in that image [15]. The user's feedback, both positive and negative, are then used to determine the degree of membership of each image to set being analyzed.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette

Calumby

Torres

2015

J Image Video Proc.

View full text Add to dashboard Cite

The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semisupervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.

show abstract

Section: Relevance Feedback Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette

Calumby

Torres

2015

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…This approach is proposed both in low-level feature spaces, and in "dissimilarity spaces", where images are represented in terms of their dissimilarities from the set of relevant images [22]. In [23], the authors propose a technique that defines a fuzzy set so that the degree of membership of each image in the repository to this fuzzy set is related to the user's interest in that image. Then, positive and negative selections are used to determine the degree of membership of each picture to this set.…”

Section: Relevance Feedback In Image Retrievalmentioning

confidence: 99%

A naive relevance feedback model for content-based image retrieval using multiple similarity measures

Arevalillo‐Herráez

Ferri

Domingo

2010

Pattern Recognition

Self Cite

View full text Add to dashboard Cite

“…The results of this learning will be applied later in order to re-rank the other images of the asked database. A large spectrum of learning algorithms, coming from pattern recognition field, have been employed: SVM [16], the genetic programming [13], fuzzy sets [3]..Etc.…”

Section: Introductionmentioning

confidence: 99%

Majority Voting Re-ranking Algorithm for Content Based-Image Retrieval

Mosbah

Boucheham

2015

Communications in Computer and Information Science

View full text Add to dashboard Cite

Abstract. We propose a new algorithm, known as Majority Voting Re-ranking Algorithm (MVRA), which re-ranks the first returned images answered by an image retrieval system. Since this algorithm proceeds to change the images rate before any visualizing to the user, it does not require any assistance. The algorithm has been experimented using the Wang database and the Google image engine and has been compared to other methods based on two clustering algorithms namely: HACM and K-means. The obtained results indicate the clear superiority of the proposed algorithm.

show abstract

A relevance feedback CBIR algorithm based on fuzzy sets

Cited by 18 publications

References 19 publications

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

A naive relevance feedback model for content-based image retrieval using multiple similarity measures

Majority Voting Re-ranking Algorithm for Content Based-Image Retrieval

Contact Info

Product

Resources

About