Perceptual color descriptor based on spatial distribution: A top-down approach

Kıranyaz, Serkan; Birinci, Murat; Gabbouj, Moncef

doi:10.1016/j.imavis.2010.01.012

Cited by 31 publications

(15 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We make use of these metrics to demonstrate the superiority of proposed method in accordance with 48 queries on Corel-1k dataset and 273 queries on Corel-10k dataset. We also tested retrieval performance of proposed dominant color cluster descriptor (DCCD) in Section 2.2 and the binary spatial map (BSM) for similarity measure in Section 2.3 against LBA [8] and perceptual color-based method (PCM) [9]. As shown in Table 1 and 2, the proposed DCCD and BSM improve the retrieval performance, and the proposed descriptor (DCCD+BSM) produces the best results.…”

Section: Resultsmentioning

confidence: 99%

Content-based image retrieval using color features of salient regions

Lee

Cho

2014

2014 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

This paper presents a content-based color image retrieval system based on color features from the salient regions and their spatial relationship. The proposed method first extracts the salient regions by a color contrast method, and finds several dominant colors for each region. Then, the spatial distribution of each dominant color is described as a binary map. Specifically, the salient region is partitioned into small sub-blocks, and each sub-block is assigned as 1 or 0 according to the number of pixels corresponding to the dominant color. The set of binary maps define the spatial distribution of dominant colors within and across the salient regions, which approximately reflect the objects' shapes and the spatial relationship of the objects. A simple matching method for this description is also proposed, which needs very few computations for each image matching. According to the experiments with several widely used color image databases, the proposed method shows better retrieval performance than the state-of-the-art and previous color-based methods. The proposed algorithm is suitable for color image retrieval on the web and mobile systems, because it needs very few computations which are mostly binary logical operations.

show abstract

Section: Resultsmentioning

confidence: 99%

Content-based image retrieval using color features of salient regions

Lee

Cho

2014

2014 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

show abstract

“…The descriptor considers both the spectral and global/local spatial features to delineate local damages with higher accuracy. Specifically, the spectral feature is derived from the RGB space quantization; while the spatial features consist of the local Gabor [26] texture quantization and global spatial color distribution [27].…”

Section: Feature Extraction and Adaptive Spectral-spatial Descriptormentioning

confidence: 99%

“…(3) Spatial color distribution (SCD) [27]. SCD is a perceptual ancillary that reflects the spatial information in a global perspective regardless of noise, image degradations, changes in size, resolution and orientation [27].…”

Section: Feature Extractionmentioning

confidence: 99%

“…SCD is a perceptual ancillary that reflects the spatial information in a global perspective regardless of noise, image degradations, changes in size, resolution and orientation [27]. SCD quantifies the spatial distribution of all specific colors by computing the spatial variance of the colors distribution, both horizontally and vertically.…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Sun

Hao²,

Chen³

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images.

show abstract

“…In formulas (22) and (23) we restored class label index c, which had been removed at the beginning of Section 2. In formula (21) t-norm and t-conorm can be chosen as min and max operators, respectively.…”

Section: Classification Of a Query Imagementioning

confidence: 99%

Fast image classification by boosting fuzzy classifiers

Korytkowski

Rutkowski

Scherer

2016

Information Sciences

132

View full text Add to dashboard Cite

This paper presents a novel approach to visual objects classification based on generating simple fuzzy classifiers using local image features to distinguish between one known class and other classes. Boosting meta learning is used to find the most representative local features. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives better classification accuracy and the time of learning and testing process is more than 30% shorter.

show abstract

Perceptual color descriptor based on spatial distribution: A top-down approach

Cited by 31 publications

References 34 publications

Content-based image retrieval using color features of salient regions

Content-based image retrieval using color features of salient regions

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Fast image classification by boosting fuzzy classifiers

Contact Info

Product

Resources

About