Comparing images using color coherence vectors

Pass, Greg; Zabih, Ramin; Miller, James

doi:10.1145/244130.244148

Cited by 623 publications

(362 citation statements)

References 20 publications

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“…mization problem is then equivalent to minimization of J with respect to L. 27 The optimization criterion J (L) can be rewritten as…”

Section: Iterative Majorizationmentioning

confidence: 99%

“…Compared 35 with the first database, the class sizes of this database have a much wider range of variations from the smallest class with 37 24 images to the largest class with 125 images. We first represent the images in the HSV color space, and 39 then compute the color coherence vector (CCV) [27] as the feature vector for each image. Specifically, we quantize each 41 image to 8 × 8 × 8 color bins, and then represent the image as a 1024-dimensional CCV ( 1 , 1 , . .…”

Section: Image Databases and Feature Representationmentioning

confidence: 99%

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Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Chang

Yeung

2006

Pattern Recognition

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7Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs 9 clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a 11 linear transformation. In this paper, we propose a new metric learning method that performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present three methods for solving 13 it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some UCI data 15 sets. Besides applying LLMA to semi-supervised clustering, we have also used it to improve the performance of content-based image retrieval systems through metric learning. Experimental results based on two real-world image databases show that LLMA significantly 17 outperforms other methods in boosting the image retrieval performance.

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“…mization problem is then equivalent to minimization of J with respect to L. 27 The optimization criterion J (L) can be rewritten as…”

Section: Iterative Majorizationmentioning

confidence: 99%

Section: Image Databases and Feature Representationmentioning

confidence: 99%

Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Chang

Yeung

2006

Pattern Recognition

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show abstract

“…Moreover to ameliorate the proposed system performance, we merged color correlograms (Huang et al, 1997) and Improved Color Coherence Vector (ICCV) (Pass et al, 1996;Chen et al, 2007) since SURF and MSER work only on grey scale images.…”

Section: Introductionmentioning

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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“…• Color Coherence Vector: this technique aims at classifying each pixel as being coherent or incoherent, being coherent pixels the ones that belong to some big connected component, while incoherent pixels are part of small connected components [30].…”

Section: Resultsmentioning

confidence: 99%

Swarm-based Descriptor Combination and its Application for Image Classification

Mansano¹,

Matsuoka²,

Abiuzzi³

et al. 2014

ELCVIA

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In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach.

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Comparing images using color coherence vectors

Cited by 623 publications

References 20 publications

Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

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

Swarm-based Descriptor Combination and its Application for Image Classification

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