Learning Color Names for Real-World Applications

Weijer, Joost van de; Schmid, Cordelia; Verbeek, Jakob; Larlus, Diane

doi:10.1109/tip.2009.2019809

Cited by 642 publications

(417 citation statements)

References 22 publications

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“…However, the performance of feature representation by means of color histograms is sill not satisfactory. Since color names show good robustness to photometric variance [26], an alternative approach is to apply color names to describe colors [12,27,10,26].…”

Section: Introductionmentioning

confidence: 99%

Salient Color Names for Person Re-identification

Yang

Yan

et al. 2014

Lecture Notes in Computer Science

387

280

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Abstract.Color naming, which relates colors with color names, can help people with a semantic analysis of images in many computer vision applications. In this paper, we propose a novel salient color names based color descriptor (SCNCD) to describe colors. SCNCD utilizes salient color names to guarantee that a higher probability will be assigned to the color name which is nearer to the color. Based on SCNCD, color distributions over color names in different color spaces are then obtained and fused to generate a feature representation. Moreover, the effect of background information is employed and analyzed for person re-identification. With a simple metric learning method, the proposed approach outperforms the state-of-the-art performance (without user's feedback optimization) on two challenging datasets (VIPeR and PRID 450S). More importantly, the proposed feature can be obtained very fast if we compute SCNCD of each color in advance.

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Section: Introductionmentioning

confidence: 99%

Salient Color Names for Person Re-identification

Yang

Yan

et al. 2014

Lecture Notes in Computer Science

387

280

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“…On the contrary, feature representations have been thoroughly investigated in the related fields of object recognition and action recognition [22,21]. Recently, Danelljan et al [6] introduced the Adaptive Color Tracker (ACT), which learns an adaptive color representation based on Color Names [33]. However, this approach still employs a standard grayscale channel for capturing image intensity information.…”

Section: Related Workmentioning

confidence: 99%

“…The channel coded color representations are compared with Color Names (CN) [33], which achieved the best results among the evaluated color features in [6]. The CN representation is inspired by linguistics.…”

Section: Channel Coded Color Representationsmentioning

confidence: 99%

“…Recently, Danelljan et al [6] evaluated several color features for visual tracking. In their evaluation, the color names representation [33] was shown to provide superior performance compared to other color features. However, the work of [6] only investigates what color feature to use, while employing raw pixel gray scale values to represent luminance information.…”

Section: Introductionmentioning

confidence: 99%

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Coloring Channel Representations for Visual Tracking

et al. 2015

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Abstract. Visual object tracking is a classical, but still open research problem in computer vision, with many real world applications. The problem is challenging due to several factors, such as illumination variation, occlusions, camera motion and appearance changes. Such problems can be alleviated by constructing robust, discriminative and computationally efficient visual features. Recently, biologically-inspired channel representations [9] have shown to provide promising results in many applications ranging from autonomous driving to visual tracking. This paper investigates the problem of coloring channel representations for visual tracking. We evaluate two strategies, channel concatenation and channel product, to construct channel coded color representations. The proposed channel coded color representations are generic and can be used beyond tracking. Experiments are performed on 41 challenging benchmark videos. Our experiments clearly suggest that a careful selection of color feature together with an optimal fusion strategy, significantly outperforms the standard luminance based channel representation. Finally, we show promising results compared to state-of-the-art tracking methods in the literature.

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“…Considering the application of our approach (retrieval of monuments pictures), we use color and feature descriptors for representing images by 92 dimension feature arrays as a combination between Colour Naming Histogram [9] (11 components) and Histogram of Gradients (81 components). Then, to assess image similarity, we compute the Euclidean distance between their corresponding feature arrays.…”

Section: Automated Image Analysismentioning

confidence: 99%

Representativeness and Diversity in Photos via Crowd-Sourced Media Analysis

Radu

Stöttinger

Ionescu

et al. 2014

Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation

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Abstract. In this paper we address the problem of user-adapted image retrieval. First, we provide a survey of the performance of the existing social media retrieval platforms and highlight their limitations. In this context, we propose a hybrid, two step, machine and human automated media analysis approach. It aims to improve retrieval relevance by selecting a small number of representative and diverse images from a noisy set of candidate images (e.g. the case of Internet media). In the machine analysis step, to ensure representativeness, images are re-ranked according to the similarity to the "most common" image in the set. Further, to ensure also the diversity of the results, images are clustered and the best ranked images among the most representative in each cluster are retained. The human analysis step aims to bridge further inherent descriptor semantic gap. The retained images are further refined via crowd-sourcing which adapts the results to human. The method was validated in the context of the retrieval of images with monuments using a data set of more than 30.000 images retrieved from various social image search platforms.

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Learning Color Names for Real-World Applications

Cited by 642 publications

References 22 publications

Salient Color Names for Person Re-identification

Salient Color Names for Person Re-identification

Coloring Channel Representations for Visual Tracking

Representativeness and Diversity in Photos via Crowd-Sourced Media Analysis

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