An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations
An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations
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DOI：
10.1109/tcsvt.2013.2242594
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发表时间：
2013-12
影响因子：
8.4
通讯作者：
Junwei Han;Sheng He;Xiaoliang Qian;Dongyang Wang;Lei Guo;Tianming Liu
中科院分区：
文献类型：
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作者：
Junwei Han;Sheng He;Xiaoliang Qian;Dongyang Wang;Lei Guo;Tianming Liu
显著性检测旨在定量预测图像中的关注位置。它可以模仿人类视觉系统的选择机制，即处理大量视觉输入的一个小子集，而忽略冗余信息。基于视觉系统V1区简单细胞的感受野类似于从自然图像中学习到的稀疏编码的生物学证据，提出了一种基于图像稀疏编码表示的显著性检测框架。与以前的许多方法致力于检查每个单独的位置的局部或全局对比度，本文开发了一个概率计算算法，通过整合对象的可能性与外观稀有。在所提出的框架中，图像稀疏编码表示产生通过学习大量的眼睛注视补丁从眼睛跟踪数据集。对象的可能性是衡量三个通用线索称为紧凑性，连续性和中心偏差。通过使用高斯混合模型来推断外观稀有度。该论文可以作为许多技术的基础，如图像/视频分割，检索，重定向和压缩。在基准数据库上进行的广泛的评估和与一些最新算法的比较证明了它的有效性。
Saliency detection aims at quantitatively predicting attended locations in an image. It may mimic the selection mechanism of the human vision system, which processes a small subset of a massive amount of visual input while the redundant information is ignored. Motivated by the biological evidence that the receptive fields of simple cells in V1 of the vision system are similar to sparse codes learned from natural images, this paper proposes a novel framework for saliency detection by using image sparse coding representations as features. Unlike many previous approaches dedicated to examining the local or global contrast of each individual location, this paper develops a probabilistic computational algorithm by integrating objectness likelihood with appearance rarity. In the proposed framework, image sparse coding representations are yielded through learning on a large amount of eye-fixation patches from an eye-tracking dataset. The objectness likelihood is measured by three generic cues called compactness, continuity, and center bias. The appearance rarity is inferred by using a Gaussian mixture model. The proposed paper can serve as a basis for many techniques such as image/video segmentation, retrieval, retargeting, and compression. Extensive evaluations on benchmark databases and comparisons with a number of up-to-date algorithms demonstrate its effectiveness.