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| 折扣與優(yōu)惠：團(tuán)購最低可5折優(yōu)惠 - 了解詳情 | 論文格式：Word格式(*.doc) |  |
摘要:現(xiàn)在做的局部特征大多數(shù)是基于角點(diǎn)和尺度空間劃分出局部的區(qū)域，提取能夠仿射不變的局部信息特征，如SIFT，然后進(jìn)行匹配，這種算法計(jì)算量非常大，要存儲(chǔ)的特征向量的長度也是非常長，通常都在上千。對(duì)于內(nèi)部細(xì)節(jié)不是很豐富的樣本，這種方法不經(jīng)濟(jì)。本文主要研究內(nèi)容檢索的目標(biāo)圖像，尤其是對(duì)分塊檢索方法進(jìn)行了研究?；诜謮K圖像特征的圖像檢索方法將目標(biāo)圖像劃分為多個(gè)分塊，每個(gè)分塊圖像特征反映了圖像的局部特征，而多個(gè)子塊圖像特征的結(jié)合又能夠?qū)^(qū)域整體形狀進(jìn)行描述，由于四叉樹分塊方法的欠缺，極坐標(biāo)下分塊方法相對(duì)準(zhǔn)確。首先對(duì)目標(biāo)圖像進(jìn)行歸一化處理，以目標(biāo)圖像的最小外接圓作為目標(biāo)區(qū)域，然后在極坐標(biāo)下分塊，對(duì)于分塊后的子圖像，我們通過實(shí)驗(yàn)充分比較這些子塊圖像的不同局部特征函數(shù)來測(cè)試比較。 關(guān)鍵詞：圖像檢索 ；最小外接圓；子塊圖像特征；極坐標(biāo)下分塊
ABSTRACT:Now most of the local features based on the angle point and scale space are carved out of the local area which can extract the local affine invariant information. Such as SIFT, and then proceed to match, but this algorithm is very large, the length of the stored feature vectors is very long, usually in the thousands, this method is not economical. In this paper, we studies mainly about the content retrieval target image, especially the block retrieval method. The image retrieval method based on image features of the block is that the target image is divided into multiple blocks. Each sub-block image features reflect the local features of the image. And the combination of multiple sub-block image features can describe the shape of the region as a whole. Due to the lack of block of the quad tree, the block method in polar coordinates is relatively accurate. First of all the target image is normalized, then see the smallest circum-circle of the target image as a target area, and divide image block into blocks in polar coordinates. For sub-image after block, through the experiment we can fully compare the accuracy of these different local characteristics of the sub-block image function. Keywords: image retrieval; smallest circum-circle; block of image features; polar coordinates block |
