本文選題：視覺(jué)顯著性 + 目標(biāo)檢測(cè)��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：人類(lèi)視覺(jué)神經(jīng)系統(tǒng)可以主觀地處理看到的自然場(chǎng)景,因?yàn)橛?jì)算能力的限制和高時(shí)效性的需求,處理過(guò)程會(huì)被簡(jiǎn)化壓縮:視神經(jīng)會(huì)選擇場(chǎng)景中吸引其注意的部分目標(biāo)率先進(jìn)行理解,這種機(jī)制被稱(chēng)作選擇性注意機(jī)制。反過(guò)來(lái)從能展示自然場(chǎng)景的圖像出發(fā),反映場(chǎng)景的圖像吸引視神經(jīng)的性質(zhì),被稱(chēng)為視覺(jué)顯著性。因此,對(duì)人類(lèi)視覺(jué)神經(jīng)系統(tǒng)進(jìn)行模擬,能夠構(gòu)造出圖像的顯著性目標(biāo)檢測(cè)模型�，F(xiàn)在,國(guó)內(nèi)外很多研究團(tuán)體都在致力于這一工作。本研究針對(duì)的圖像類(lèi)型是自然場(chǎng)景類(lèi)的圖像,從顏色空間對(duì)比度信息的角度深入探索,主要完成了以下工作:針對(duì)已有的基于色彩空間對(duì)比度信息的顯著性檢測(cè)方法進(jìn)行了優(yōu)化。已知減少顏色數(shù)量能夠極大加快算法運(yùn)行效率,指出已有方法在減少顏色數(shù)量上存在一定的瑕疵:簡(jiǎn)單的把RGB三個(gè)通道各自進(jìn)行了量化限制,沒(méi)有從圖像整體的角度針對(duì)顏色統(tǒng)計(jì)進(jìn)行量化,這就有可能造成量化后圖像一定程度上的失真。明確問(wèn)題后,提出把八叉樹(shù)運(yùn)用到顏色表示中,用以統(tǒng)計(jì)顏色出現(xiàn)頻率,進(jìn)而形成調(diào)色板完成顏色量化的工作。然后經(jīng)過(guò)直方圖加速和去噪平滑操作后得到最終的顯著圖,因?yàn)槭д媲闆r已有改善,故可以使顯著圖較原方法效果更好,而且因?yàn)槭褂昧税瞬鏄?shù)算法,在時(shí)間復(fù)雜度上也會(huì)有所優(yōu)化。將本文方法同幾種不同原理的經(jīng)典算法進(jìn)行了對(duì)比,結(jié)果證明本文方法效果較好,同時(shí)在時(shí)間成本上有一定優(yōu)勢(shì)。將模糊C均值聚類(lèi)算法應(yīng)用于圖像處理中,根據(jù)聚類(lèi)結(jié)果進(jìn)行分割。模糊C均值聚類(lèi)(FCM)算法是一種基于模糊理論的軟聚類(lèi)算法,將該算法應(yīng)用到圖像分割當(dāng)中,可以獲得比傳統(tǒng)硬聚類(lèi)算法更好的效果。對(duì)比分析原始圖像進(jìn)行基于FCM算法的分割中選擇各個(gè)參數(shù)的不同情況,提出將顯著性檢測(cè)結(jié)果與基于FCM算法的分割過(guò)程相結(jié)合,通過(guò)結(jié)果證明,的確可以得到更好地效果。本文提出的方法可以勝任通用顯著性目標(biāo)檢測(cè)工作,且在效果和時(shí)間方面有較為出色的表現(xiàn);顯著性檢測(cè)與FCM圖像分割算法的結(jié)合應(yīng)用提升了兩者的效果;本文的工作可以作為圖像內(nèi)容感知和圖像檢索等應(yīng)用的基礎(chǔ)。
[Abstract]:The human visual nervous system can handle the natural scene subjectively, because of the limitation of computational power and the need for high timeliness, the process will be simplified: the optic nerve will choose the part of the scene that attracts attention in the scene to take the lead in understanding. This mechanism is called the selective attention mechanism. In turn, the natural field can be displayed. The image of the scene, which reflects the nature of the optic nerve, is called the visual significance. Therefore, it simulates the human visual nervous system and can construct a significant target detection model of the image. Now, many research groups at home and abroad are working on this work. The image type is a natural scene class. From the point of view of color space contrast information, the following work has been completed: the significant detection method based on the color space contrast information has been optimized. It is known that reducing the number of colors can greatly speed up the operation efficiency of the algorithm, and points out that the existing methods have some defects in reducing the number of colors. Fault: the quantitative restriction of the three channels of the RGB is simply carried out, and the color statistics are not quantified from the angle of the whole image. This may cause a certain degree of distortion of the quantized image. After the problem, the octree is applied to the color representation to count the frequency of color, and then the color palette is completed. The work of color quantization. Then after the histogram acceleration and de-noising smooth operation, the final significant graph is obtained, because the distortion has been improved, so the significant image can be better than the original method, and because of the octree algorithm, the time complexity will also be optimized. This method is the same with several different principles of the classic. The algorithm has been compared. The results show that the method has good effect and has some advantages in time cost. Fuzzy C means clustering algorithm is applied to image processing and segmentation according to clustering results. Fuzzy C mean clustering (FCM) algorithm is a soft clustering algorithm based on fuzzy theory, and the algorithm is applied to image segmentation. We can obtain better results than traditional hard clustering algorithms. Compare and analyze the different conditions of the selection of the original image based on FCM algorithm, the results are combined with the segmentation process based on the FCM algorithm, and the results prove that the results can be better. The method proposed in this paper can win. The work of universal significant target detection has been performed well in effect and time; the combination of significant detection and FCM image segmentation improves the effect of both. The work of this paper can be used as the basis for the application of image content perception and image retrieval.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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