This paper presents a general framework for seamlessly combining multiple low cost and inaccurate estimated segmentation maps (with an arbitrary number of regions) of the same scene to achieve a final improved segmentation. The proposed fusion model is derived from the well-known precision-recall criterion, specially dedicated to the specific clustering problem of any spatially indexed data and which is also efficient and widely used in the vision community for evaluating both a region-based segmentation and the quality of contours produced by this segmentation map compared to one or multiple ground-truth segmentations of the same image. The proposed combination framework is here specifically designed to be robust with respect to outlier segmentations (that appear to be inconsistent with the remainder of the segmentation ensemble) and includes an explicit internal regularization factor reflecting the inherent ill-posed nature of the segmentation problem. We propose also a hierarchical and efficient way to optimize the consensus energy function related to this fusion model that exploits a simple and deterministic iterative relaxation strategy combining the different segments or individual regions belonging to the segmentation ensemble in the final solution. The experimental results on the Berkeley database with manual ground truth segmentations show the effectiveness of our combination model.

http://www.iro.umontreal.ca/~mignotte/Publications/IJSIP14_Helou.pdf

A Precision-Recall Criterion Based Consensus Model For Fusing Multiple Segmentations

Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called “technique for order performance by similarity to ideal solution”. Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem

This paper presents a suggested framework for video matching based on local features extracted from the DCimage of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the realtime margin. There are also various optimisations that can be done to improve this computation complexity.

Video matching using DC-image and local features

Content-Based Image Retrieval is the high level of the retrieval.This paper introduce and compare various methods for feature extraction and similarity measurement.In the end,the hot research and future research trends are presented.

Research of Content-Based Image Retrieval

Soluble sugar content is a key component in controlling fruit flavor, and its accumulation in fruit is largely determined by sugar metabolism and transportation. When the diurnal temperature range is greater, the fleshy fruits accumulated more soluble sugars and become more sweeter. However, the molecular mechanism underlying this response remains largely unknown. In this study, we verified that low-temperature treatment promoted soluble sugar accumulation in apple fruit and found that this was due to the upregulation of the Tonoplast Sugar Transporter genes MdTST1/2. A combined strategy using assay for transposase-accessible chromatin (ATAC) sequencing and gene expression and cis-acting elements analyses, we identified two C-repeat Binding Factors, MdCBF1 and MdCBF2, that were induced by low temperature and that might be upstream transcription factors of MdTST1/2. Further studies established that MdCBF1/2 could bind to the promoters of MdTST1/2 and activate their expression. Overexpression of MdCBF1 or MdCBF2 in apple calli and fruit significantly upregulated MdTST1/2 expression and increased the concentrations of glucose, fructose, and sucrose. Suppression of MdTST1 and/or MdTST2 in an MdCBF1/2-overexpression background abolished the positive effect of MdCBF1/2 on sugar accumulation. In addition, simultaneous silencing of MdCBF1/2 downregulated MdTST1/2 expression and apple fruits failed to accumulate more sugars under low-temperature conditions, indicating that MdCBF1/2-mediated sugar accumulation was dependent on MdTST1/2 expression. Hence, we concluded that the MdCBF1/2-MdTST1/2 module is crucial for sugar accumulation in apples in response to low temperatures. Our findings provide mechanistic components coordinating the relationship between low temperature and sugar accumulation as well as new avenues to improve fruit quality.

The MdCBF1/2-MdTST1/2 module regulates sugar accumulation in response to low temperature in apple.

Genome-wide identification and function analysis of the sucrose phosphate synthase MdSPS gene family in apple

Response of carbohydrate metabolism-mediated sink strength to auxin in shoot tips of apple plants

In this paper, we propose an effective frame of shot segmentation in compress-domain videos. Firstly, we extract DC coefficients of I-frames in compressed-domain, and construct a sequence of DC-images which are on the basis of DC coefficients in I-Frames. Secondly, the difference between two adjacent DC-images is calculated by integrating grid-mapping dynamic window, color moments and spatial distribution entropy of colors. It comes true that most shot boundaries are pre-detected. Finally, more accurate shot boundaries or wrong shot boundaries are detected by the analysis of Macro-block in P or B frame between two adjacent I-Frames on a detected segmentation position. The experiments show that the frame efficiently has improved the performance of shot detection, and to a certain extent, the complexity of the shot detection can be reduced.

An effective framework of shot segmentation based on I-Frame in compressed-domain videos

Texture contains high and low frequency information which could be hierarchically extracted by scattering the texture along multiple paths, with a cascade of wavelet modulus operators implemented in a deep convolutional network, which builds a scattering energy distribution network. Therefore, the scattering transform is used, in this paper, to get texture energy features. Besides, the classification of scattering energy feature matrix at all levels is done by using the Ostu global threshold processing method. Experimental results indicate that high accuracy can be achieved for both texture segmentation and license plate location with the proposed methods.

Texture Segmentation via Scattering Transform

The improved algorithm selects expected locations (for adding navigation links) in backtracks set at the point of the earlier and the less backtracks, which avoids effectively negative impact to the accuracy of the overall analysis by the long access sequence. The experimental results show that the improved algorithm can find expected pages effectively, thus can achieve the target of adjustment and reorganization of website.

An Improved Website Structure Optimizing Algorithm

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