Efficient saliency analysis based on wavelet transform and entropy theory

Ma, Xiaolong; Xie, Xudong; Lam, Kin‐Man; Zhong, Yisheng

doi:10.1016/j.jvcir.2015.04.008

Cited by 17 publications

(9 citation statements)

References 46 publications

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“…The selected existing methods are IT in Itti et al [4], VS in Li et al [15], SR in Hou and Zhang [13], SE in Murray et al [18], WT in Imamoglu et al [3], ST in Bao et al [26], WE in Ma et al [20], PQFT in Guo and Zhang [35], HSC in Li et al [14] and DQCT in Boris and Stiefelhagen [16]. As discussed earlier, existing models were proposed in different domains.…”

Section: Performance Analysismentioning

confidence: 99%

“…WT can represent singularities in a much better manner than what Fourier transform can. Ma et al [20] proposed saliency model based on WT and entropy. Murray et al [18] computed weight maps for each colour sub-bands using Wavelet coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method is compared with 10 state-of-the-art methods, namely, IT in Itti et al [4], SR in Hou and Zhang [13], SE in Murray et al [18], WT in Imamoglu et al [3], VS in Li et al [15], ST in Bao et al [26], WE in Ma et al [20], PQFT in Guo and Zhang. For a fair evaluation, only frequency-based state-of-the-art models have been taken for comparison.…”

Section: Introductionmentioning

confidence: 99%

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An efficient visual saliency detection model based on Ripplet transform

Andrushia

Thangarajan²

2017

Sādhanā

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Even though there have been great advancements in computer vision tasks, the development of human visual attention models is still not well investigated. In day-to-day life, one can find ample applications of saliency detection in image and video processing. This paper presents an efficient visual saliency detection model based on Ripplet transform, which aims at detecting the salient region and achieving higher Receiver Operating Characteristics (ROC). Initially the feature maps are obtained from Ripplet transform in different scales and different directions of the image. The global and local saliency maps are computed based on the global probability density distribution and feature distribution of local areas, which are combined together to get the final saliency map. Ripplet-transform-based visual saliency detection is the novel approach carried out in this paper. Experimental results indicate that the proposed method based on Ripplet transformation can give excellent performance in terms of precision, recall, F measure and Mean Absolute Error (MAE), and is compared with 10 state-of-the-art methods on five benchmark datasets.

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Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An efficient visual saliency detection model based on Ripplet transform

Andrushia

Thangarajan²

2017

Sādhanā

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“…Ma [33] constructed saliency map based on wavelet domain and the entropy weights. The inverse Wavelet transform (IWT) is not performed.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification

Liu

Dong

et al. 2017

Remote Sensing

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Abstract:Ship detection by Unmanned Airborne Vehicles (UAVs) and satellites plays an important role in a spectrum of related military and civil applications. To improve the detection efficiency, accuracy, and speed, a novel ship detection method from coarse to fine is presented. Ship targets are viewed as uncommon regions in the sea background caused by the differences in colors, textures, shapes, or other factors. Inspired by this fact, a global saliency model is constructed based on high-frequency coefficients of the multi-scale and multi-direction wavelet decomposition, which can characterize different feature information from edge to texture of the input image. To further reduce the false alarms, a new and effective multi-level discrimination method is designed based on the improved entropy and pixel distribution, which is robust against the interferences introduced by islands, coastlines, clouds, and shadows. The experimental results on optical remote sensing images validate that the presented saliency model outperforms the comparative models in terms of the area under the receiver operating characteristic curves core and the accuracy in the images with different sizes. After the target identification, the locations and the number of the ships in various sizes and colors can be detected accurately and fast with high robustness.

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“…그러나 이 방법은 전역 영상에 대한 정보 모델에 고려 가 없는 단점이 있다. 웨이블릿 변환과 엔트로피 이론 에 근거한 세일리언시 검출 방법은 2차원 엔트로피를 가중치로 하여 웨이블릿 계수가 표현하는 국부적 세일 리언시 특징을 종합하는 방법 [16] 으로, 적은 계산 량을 가지는 장점이 가지고 있다. 2015년 Ma 등은 특이값 분 해를 이용한 세일리언시 검출 방법을 발표하였다 [17] .…”

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Saliency Detection Using Entropy Weight and Weber's Law

Lee¹,

Moon²,

Eom³

2017

Journal of the Institute of Electronics and Information Enginee

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In this paper, we present a saliency detection method using entropy weight and Weber contrast in the wavelet transform domain. Our method is based on the commonly exploited conventional algorithms that are composed of the local bottom-up approach and global top-down approach. First, we perform the multi-level wavelet transform for the CIE Lab color images, and obtain global saliency by adding the local Weber contrasts to the corresponding low-frequency wavelet coefficients. Next, the local saliency is obtained by applying Gaussian filter that is weighted by entropy of wavelet high-frequency subband. The final saliency map is detected by non-lineally combining the local and global saliencies. To evaluate the proposed saliency detection method, we perform computer simulations for two image databases. Simulations results show the proposed method represents superior performance to the conventional algorithms.

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Efficient saliency analysis based on wavelet transform and entropy theory

Cited by 17 publications

References 46 publications

An efficient visual saliency detection model based on Ripplet transform

An efficient visual saliency detection model based on Ripplet transform

Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification

Saliency Detection Using Entropy Weight and Weber's Law

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