图像相似度和 k 均值聚类
我正在玩一点图像相似性。事实上我正在玩图像检索系统。理想情况下，我想创建某种图像索引，我可以查询它以获得相似的图像。
我当前的想法是将某种 ImageDescriptor 存储到索引中，每个描述符可以在其中具有不同的特征，例如 k-mean-cluster-centroids、直方图......并且我有一些简单的基于权重的计算 - 每个特征都有距离函数该函数的结果乘以它的权重并对所有特征求和。最终总和是与我的图像的距离。不确定这个思路好不好？
所以我开始玩直方图。我存储了直方图的索引，然后查询它们的直方图和索引存储的直方图之间的距离。它提供了某种相似性，但在大多数情况下远非理想。
现在我正在研究 k 均值聚类。我已经实现了基于 RGB 距离的分割（也会在 Lab 颜色模式下尝试）。我的索引由质心向量（来自聚类）组成。现在我正在做质心之间的最小距离比较。它给出了更好的结果，但也远非好。
我的第一个问题是，我可以对段（簇）做一些比查询距离更好的事情吗？我如何包含形状信息？
顺便说一句，大多数图像都是日常物品的图像（不同的铅笔、不同的眼镜、不同的鞋子……），并且在相同颜色的背景上具有不同的纹理。没有自然的图像、面孔、树木、云彩、山脉……
问候 扎哈里杰
I'm playing a little bit with image similarity. In fact i'm playing with image retrieval system. Ideally i wanna to create some kind of image index that I can query to get similar images.
My current thought is to store some kind of ImageDescriptor into index and each descriptor can have different features in it, e.g. k-mean-cluster-centroids, histograms, ... And i have some simple wight based calculation - each feature has distance function and result of that function is multiplied by it's wight and summed across all features. Final sum is distance from my image. Not sure is this good line of thought?
So i started to play with histograms. I stored index of histograms, than query them for distance between histogram and index stored histograms. It gives some kind of similarity but in most case is far from good ideal.
Now I'm playing with k-mean clustering. I already implemented segmentation based on RGB distance (will try also in Lab color mode). My index consists of vector of centroids (from clustering). Now I'm doing just min-distance comparison between centroids. It give better results but also far from good.
My first question can i do something better with segments (clusters) than to query for distance? How can i include shape information?
Just as side note, most images are images of everyday objects (different pencils, different glasses, different shoes, ...) and with different textures on the background of same color. No natural images, faces, trees, clouds, mountains, ...
Regards
Zaharije
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图像相似度不仅仅基于像素。图像相似度有几个维度。为了获得良好的相似性，您需要从图像中获得额外的信息。低级功能等
image similarity is not only pixel based. There ara several dimensions for image similarity. For good similarity, you need to have extra informs from images. Low level features etc.