SIMPLIcity: semantics-sensitive integrated matching for picture libraries

Wang, James; Li, Jia; Wiederhold, Gio

doi:10.1109/34.955109

Cited by 1,826 publications

(697 citation statements)

References 27 publications

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“…The first dataset is a subset of the Corel-1000 images (Wang et al, 2001). It consists of 10 classes, each of 100 images.…”

Section: Datasetsmentioning

confidence: 99%

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Elnemr

2016

Journal of Computer Science

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Content-Based Image Retrieval (CBIR) has received an extensive attention from researchers due to the rapid growing and widespread of image databases. Despite the massive research efforts consumed for CBIR, the completely satisfactory results have not yet been attained. In this article, we offer a new CBIR technique that relies on extracting Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) feature descriptors as well as the color features; color correlograms and Improved Color Coherence Vector (ICCV). These features are joined and used to build a multidimensional feature vector. Bag-of-Visual-Words (BoVW) technique is utilized to quantize the extracted feature vector. Then, a multiclass Support Vector Machine (SVM) is implemented to classify the query images. The performance of the presented retrieval framework is analyzed and scrutinized by comparing it with three alternative approaches. The first one is based on extracting SURF descriptors while the second one is based on extracting SURF descriptors, color correlograms and ICCV. The third approach, on the other hand, is based on extracting MSER, color correlograms and ICCV. All implemented schemes are tested on two benchmark datasets; Corel-1000 and COIL-100 datasets. The empirical results show that our suggested approach has a superior discriminative classification and retrieval performance with respect to other approaches. The proposed method achieves average precisions of 88 and 93% for the Corel-1000 and COIL-100 datasets, respectively. Moreover, the proposed system has shown a substantial advance in the retrieval precision when compared with other existing systems.

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“…The first dataset is a subset of the Corel-1000 images (Wang et al, 2001). It consists of 10 classes, each of 100 images.…”

Section: Datasetsmentioning

confidence: 99%

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Elnemr

2016

Journal of Computer Science

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“…Wang's (James et al, 2001) dataset comprises of 1000 Corel images with ground truth. The image collection in 1000 images is divided to 10 categories with each category comprising of 100 images.…”

Section: Resultsmentioning

confidence: 99%

Color Perception Histogram for Image Retrieval Using Multiple Similarity Measures

Malini¹

2014

Journal of Computer Science

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This study aims to increase the retrieval efficiency of proposed image retrieval system on the basis of color content. A new idea of feature extraction based on color perception histogram is proposed. First, the color histogram is constructed for HSV image. Secondly, the true color and grey color components are identified based on hue and intensity. The weight for true and grey color components is calculated using NBS distance. An updated histogram is constructed using weighted true and grey color values. The color features extracted from the updated histogram of query image and for all the images in image database are compared with existing color histogram based technique by using multiple similarity measures. Experimental results show that proposed image retrieval based on the color perception histogram gives higher retrieval performance in terms of high average precision and average recall with less computational complexity.

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“…Corel_1k Dataset: Corel_1k dataset is commonly used in image retrieval researches such as SIMPLIcity [10] . It consists of 1000 images.…”

Section: Experimental Environmentmentioning

confidence: 99%

Fixed Representative Colors Feature Extraction Algorithm for Moving Picture Experts Group-7 Dominant Color Descriptor

Ibrahim¹,

Sahawneh²,

Makhadmeh³

et al. 2009

J. of Computer Science

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Problem statement: MPEG-7 has defined a set of color descriptors for the multimedia contents; one of these descriptors is the Dominant Color Descriptor (DCD). The DCD adopted the Generalized Lloyd Algorithm (GLA) as a feature extraction technique. However, using this algorithm in image retrieval systems may produce perceptually irrelevant images in the ranked results. Approach: In this study, we proposed a new technique for the extraction of the DCD called Fixed Representative Colors Feature Extraction Algorithm (FRCFE). This technique is based on converting the original image colors to a set of predefined colors in the RGB color space. Also, we developed an image retrieval system to measure the effectives of this technique. Results: Experimental results revealed that FRCFE provides better accurate and perceptually relevant image retrieval for the MPEG-7 DCD than GLA in the chosen dataset. Conclusion: Mapping the original image colors to a set of predefined colors preserved the main features of the image and can produce more accurate results when used in image retrieval systems.

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SIMPLIcity: semantics-sensitive integrated matching for picture libraries

Cited by 1,826 publications

References 27 publications

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Color Perception Histogram for Image Retrieval Using Multiple Similarity Measures

Fixed Representative Colors Feature Extraction Algorithm for Moving Picture Experts Group-7 Dominant Color Descriptor

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