Experiment with reduced feature vector in CBIR system with relevance feedback

Zajiae, G.; Kojiae, N.; Reljin, Nikola; Reljin, Branimir

doi:10.1049/cp:20080304

Cited by 6 publications

(5 citation statements)

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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%

“…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: Introductionmentioning

confidence: 99%

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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“…Comparing the obtained results with other feature extraction approaches (Mean-Shift and Gaussian Mixtures based on Weighted Color Histograms [16], Reduced Feature Vector with Relevance Feedback [61] and SIFT based Gaussian Naïve Bayesian Network [62]), DITEC shows the best performance for most categories (Figure 13) and the highest mean precision value. Other performance parameters (such as recall, F-Measure) have not been compared since they have not be indicated in the papers related with the rest of the methods.…”

Section: Case Study 1: Corel 1000 Datasetmentioning

confidence: 96%

Trace transform based method for color image domain identification

Olaizola¹,

Quartulli²,

Flórez³

et al. 2012

Preprint

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Context categorization is a fundamental pre-requisite for multi-domain multimedia content analysis applications . Most feature extraction methods require prior knowledge to decide if they are suitable for a specific domain and to optimize their input parameters. In this paper, we introduce a new color image context categorization method (DITEC) based on the trace transform. We also analyze the distortions produce by the parameters that determine the sampling of the discrete trace transform. The problem of dimensionality reduction of the obtained trace transform signal is addressed through statistical descriptors that keep the underlying information. Moreover, Feature Subset Selection (FSS) is applied to both, improve the classification performance and compact the final length of the descriptor. These extracted features offer a highly discriminant behavior for content categorization. The theoretical properties of the method are analyzed and experimentally validated through two different datasets.

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“…Approaches for reduct-based result generation based on relevance feedback are discussed in [36][37][38][39]. The idea is to use the information provided by user at each retrieval pass in the next retrieval pass in an efficient way to reduce the size of search set before applying the next search.…”

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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Experiment with reduced feature vector in CBIR system with relevance feedback

Cited by 6 publications

References 0 publications

Comparison of CBIR Systems with Different Number of Feature Vector Components

Comparison of CBIR Systems with Different Number of Feature Vector Components

Trace transform based method for color image domain identification

Intelligent Image Retrieval Techniques: A Survey

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