Relevance feedback in content-based image retrieval: bayesian framework, feature subspaces, and progressive learning

Su, Zhong; Zhang, Hong-Jiang; Li, S.; Ma, Shaoping

doi:10.1109/tip.2003.815254

Cited by 166 publications

(8 citation statements)

References 13 publications

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“…Many researchers have tried to devise a framework using which some of the common image retrieval tasks can be performed. In [28][29][30][31][32][33] Nonlinear heterogeneous shape texture and intensity features based relevance feedback learning scheme for efficient image retrieval is discussed in [34]. System is made in such a way that it adapts the variance of users and applications using relevance feedback approach.…”

Section: Relevance Feedback System For Cbirmentioning

confidence: 99%

Intelligent Image Retrieval Techniques: A Survey

Yasmin¹,

Mohsin²,

Sharif³

2014

Journal of Applied Research and Technology

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In the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same contentbased searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.

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Section: Relevance Feedback System For Cbirmentioning

confidence: 99%

Intelligent Image Retrieval Techniques: A Survey

Yasmin¹,

Mohsin²,

Sharif³

2014

Journal of Applied Research and Technology

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“…Final results are obtained by combining rank from the individual results from all SOM. Relevance feedback [20,21] is a well-known concept in the area of image retrieval for incorporating users' feedback by interactive process to enhance retrieval results. The use of relevance feedback within SOM framework [17] was shown to be useful for interactive face matching system.…”

Section: Introductionmentioning

confidence: 99%

Face Matching in Large Database by Self-Organizing Maps

Chow

Rahman

2006

Neural Process Lett

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A novel self-organizing map (SOM) based retrieval system is proposed for performing face matching in large database. The proposed system provides a small subset of faces that are most similar to a given query face, from which user can easily verify the matched images. The architecture of the proposed system consists of two major parts. First, the system provides a generalized integration of multiple feature-sets using multiple self-organizing maps. Multiple feature-sets are obtained from different feature extraction methods like Gabor filter, Local Autocorrelation Coefficients, etc. In this platform, multiple facial features are integrated to form a compressed feature vector without concerning scaling and length of individual feature set. Second, an SOM is trained to organize all the face images in a database through using the compressed feature vector. Using the organized map, similar faces to a query can be efficiently identified. Furthermore, the system includes a relevance feedback to enhance the face retrieval performance. The proposed method is computationally efficient. Comparative results show that the proposed approach is promising for identifying face in a given large image database.

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“…There are many documents on image retrieval with relevance feedback [16,4,10,21,8]. However, as described in [22], most of them involve category search with many samples for each category; moreover, in many cases, some low level features, such as texture, color and shape, are sufficient to distinguish among different categories.…”

Section: Introductionmentioning

confidence: 99%

An interactive system for mental face retrieval

Fang

Geman

Boujemaa

2005

Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval

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We propose a system to "retrieve" the mental image of a face from a large database using Bayesian inference and relevance feedback. Since the "target image" exists only in the mind of the user, mental image retrieval differs sharply from standard, example-based retrieval and has not been widely studied. In designing the relevance feedback engine, we adopt probabilistic models for the display and answer processes. The answer model is designed to capture properties of human cognition in choosing among displayed faces. The images in each display are selected according to heuristics inspired by maximizing the conditional mutual information between the answer and the target given the previous feedback. Simulations and real tests validate show that the relevance feedback engine operates in real-time and locates the target in a reasonable number of displays.

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Relevance feedback in content-based image retrieval: bayesian framework, feature subspaces, and progressive learning

Cited by 166 publications

References 13 publications

Intelligent Image Retrieval Techniques: A Survey

Intelligent Image Retrieval Techniques: A Survey

Face Matching in Large Database by Self-Organizing Maps

An interactive system for mental face retrieval

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