FLIC: Fast linear iterative clustering with active search

Abstract

In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm named "active search" which explicitly considers neighboring continuity. Based on this search method, we design a back-and-forth traversal strategy and a "joint" assignment and update step to speed up the algorithm. Compared to earlier works, such as Simple Linear Iterative Clustering (SLIC) and its follow-ups, who use fixed search regions and perform the assignment and the update step separately, our novel scheme reduces the number of iterations required for convergence, and also improves the boundary sensitivity of the over-segmentation results. Extensive evaluations on the Berkeley segmentation benchmark verify that our method outperforms competing methods under various evaluation metrics. In particular, lowest time cost is reported among existing methods (approximately 30 fps for a 481 × 321 image on a single CPU core). To facilitate the development of over-segmentation, the code will be publicly available.

Superpixel Segmentation by Forgetting Geodesic Distance

“…5. In terms of BR, our method outperforms other stateof-the-art methods except FLIC [29]. It can be seen that our method performs better than those competing methods, such as LSC [20] and SNIC [21].…”

“…Meanwhile, computational efficiency becomes its bottleneck due to measuring the geodesic distance and iterative updating. Recently, Zhao et al and Hu et al propose two superpixel segmentation methods based on raster scanning, i.e., MBS [28] and FLIC [29], to improve computation efficiency. The former uses a coarseto-fine hierarchical scheme to estimate superpixel centers.…”

“…To obtain an object and comprehensive comparison, we test our method by three different metric measures, which are the common quality indications in existing works. Meanwhile, we compare our method with nine state-of-the-art methods: FH [12], SLIC [16], TP [15], NC [11], LSC [20], RPS [24], SNIC [21], MBS [28] and FLIC [29]. We execute segmentation based on publicly available codes provided by authors.…”

A New Trend of Digital Healthcare in 3D Printed Medicines

“…In addition, HME as well as FDM, both have shown a change in the physical nature of the drug molecules (such as crystalline/ non-crystalline conditions), thus affecting their stability, melting rates and solubility. Analytical testing is necessary for predicting drug performances (Zhao J et al, 2018). Nevertheless, methods of destructive characterization, for instance, high-performance liquid chromatography (HPLC), in vitro fertilization as well as dissociation testing, and ultraviolet spectroscopy, cannot be used in this scenario.…”

Scoot: A Perceptual Metric for Facial Sketches

“…Comparison of data is considered to be the most important step [80,91], especially in image processing [5,8,89]. For various end-user applications such as face sketch [49], image style transfer [27], image quality assessment [66], saliency detection [11][12][13]90], segmentation [41][42][43] and disease classification [73], image denoising [71], the comparison can turn out to be evaluating a "perceptual distance", which assesses how similar two images are in a way that highly correlates with human perception.…”