Accelerating of Image Retrieval in CBIR System with Relevance Feedback

Zajic, Goran; Kojić, Nenad; Radosavljević, Vladan; Rudinac, Maja; Rudinac, Stevan; Reljin, Nikola; Reljin, Irini; Reljin, Branimir

doi:10.1155/2007/62678

Cited by 13 publications

(10 citation statements)

References 41 publications

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“…Results obtained by using 25-component FVs are compared with results when using the same CBIR engine but with the full-length FV of 556 components inspired by MPEG-7 [19][20], and with the system using feature vector reduction of about 90% [21,22]. Full-length FVs describe the color (dominant colors in HSV and YCbCr spaces, with 32 components each), color histogram (HSV, 164 components, and YCbCr, 177 components), histogram of line directions (73 components), Gabor texture features (62 coordinates) and gray-level co-occurrence matrix (16 components).…”

Section: Cbir System Evaluation and Resultsmentioning

confidence: 99%

“…We analyzed the retrieval with full-length FVs [20], retrieval with feature vector reduction [22] of about 90% (using about 50 automatically selected components from a full-length FVs), and the retieval with the proposed 25-component FVs. As noted earlier, retrieval quality is evaluated subjectively, by three independent users, and results are averaged.…”

Section: Cbir System Evaluation and Resultsmentioning

confidence: 99%

“…In our previous work we have suggested several methods targeted to accelerate search and retrieval in large image databases, without significant degradation of retrieving accuracy: the adaptive clustering of image database (ACID) system [19], a system which uses minor-component analysis (MCA) [20], and a system with feature vector reduction (FVR) [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of CBIR Systems with Different Number of Feature Vector Components

Líu

2007

Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)

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Content-based image retrieval (CBIR) systems with user relevance feedback are considered. The influence of the type and the number of feature vector (FV) components on the retrieval efficiency was investigated. We compared a CBIR system with a very small number of FV components (only 25 components describing color and texture) with a system with a high-dimensional FV inspired by MPEG-7 (556 coordinates describing color, texture and line directions), as well as with a system using feature vector reduction (FVR) of about 90% (with about 50 FV components from the full-length 556-component FVs). The systems are tested over the annotated Corel 1K and Corel 60K datasets. Simulation results show that a decreased number of FV components does not have significant influence on the quality of image retrieval, while the processing time is reduced compared to CBIR with full-length FV and/or FVR.

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Section: Cbir System Evaluation and Resultsmentioning

confidence: 99%

Section: Cbir System Evaluation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of CBIR Systems with Different Number of Feature Vector Components

Líu

2007

Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)

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“…This abstract representation allows for faster and safer comparisons of similarities between images. Especially in CBIR tasks, where the objective is not to find the one and only similar image but a set of k top correctly retrieved results, this discrete domain of features minimizes classification errors [43].…”

Section: The Global Descriptors Employed To Be Localizedmentioning

confidence: 99%

Localizing global descriptors for content-based image retrieval

Iakovidou

Anagnostopoulos

Kapoutsis

et al. 2015

EURASIP J. Adv. Signal Process.

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In this paper, we explore, extend and simplify the localization of the description ability of the well-established MPEG-7 (Scalable Colour Descriptor (SCD), Colour Layout Descriptor (CLD) and Edge Histogram Descriptor (EHD)) and MPEG-7-like (Color and Edge Directivity Descriptor (CEDD)) global descriptors, which we call the SIMPLE family of descriptors. Sixteen novel descriptors are introduced that utilize four different sampling strategies for the extraction of image patches to be used as points of interest. Designing with focused attention for content-based image retrieval tasks, we investigate, analyse and propose the preferred process for the definition of the parameters involved (point detection, description, codebook sizes and descriptors' weighting strategies). The experimental results conducted on four different image collections reveal an astonishing boost in the retrieval performance of the proposed descriptors compared to their performance in their original global form. Furthermore, they manage to outperform common SIFTand SURF-based approaches while they perform comparably, if not better, against recent state-of-the-art methods that base their success on much more complex data manipulation.

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“…Moreover, clusters are adaptively updated after each retrieving step, following actual user's needs. In [12] we proposed the FVR (Feature Vector Reduction) technique. Initially, feature vectors are of high dimensionality.…”

Section: Introductionmentioning

confidence: 99%

The use of unlabeled data in image retrieval with relevance feedback

Radosavljević

Kojić²,

Zajic³

et al. 2008

2008 9th Symposium on Neural Network Applications in Electrical Engineering

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This paper describes a content-based image retrieval (CBIR) system which makes use of both labeled images, annotated by the user, and unlabeled images available in the database. The system initially retrieves images objectively closest to the query image. The user then subjectively labels retrieved images as relevant or irrelevant. Although such relevance feedback from the user is an effective way of bridging the semantic gap between objective and subjective similarity, it is also very time consuming, requiring huge human effort. Often, the number of labeled images is very small. In an inductive approach the labeled set of images is used for training a CBIR system while the large set of unlabeled images remains unused. In this paper we exploit the transductive Support Vector Machine (SVM) algorithm as a way of taking advantage of unlabeled data in CBIR. Our findings are compared to the results of an inductive SVM. We draw some conclusions as to when the use of unlabeled data might be helpful. The considered systems are tested over images from the CorellK dataset.

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Accelerating of Image Retrieval in CBIR System with Relevance Feedback

Cited by 13 publications

References 41 publications

Comparison of CBIR Systems with Different Number of Feature Vector Components

Comparison of CBIR Systems with Different Number of Feature Vector Components

Localizing global descriptors for content-based image retrieval

The use of unlabeled data in image retrieval with relevance feedback

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