I think if images could be labeled automatically, Google would have abandoned the image labeler a long time ago. Unfortunately, computers have a lot of trouble understanding images.
Edit:
If you are interested in computer vision research have a look at CVPapers, especially Open Source Computer Vision Implementations. Automatic image labeling is far from being solved (unless you have a very specific/restricted set of topics).
The words “Larry Page” and “Sergey Brin” appear near images of Eric Schmidt, or in image captions, or in links to those images. Google makes a guess that the words are related to the image. Google technology isn’t yet to the point where it can tell what’s in an image by looking at it directly.
HTH, don't get your hopes up too high.
P.S.: I hope you (or someone else) proves me wrong and shares it here with me ;-)
Edit2:
I just stumbled across the Voc 2010 Challenge, which, in my opinion, illustrates very well the current state of computer vision advances. In one of the challenges the contestants have to find an object (from a very limited set of objects) in the image and classify it. On the result page you can see, that one of the algorithms manages to classify air plane with a 93% accuracy, but "fails" at other categories.
That is just for the quest to find the "things", not even adjectives or emotions.
Check out https://imagga.com/
It has some impressive results. Also some wildly entertaining results... Thankfully all tags generated come with a confidence value, so you could always ignore anything less than a threshold (~15% for my use case).
12,000 images a month for free, not bad. If you have over 12,000 month images then just queue your requests.
基于网络的 API 中的一个选项是 vize.it，它提供了一个网络界面您可以在其中上传示例图像并为其添加标签，它可以让您训练自己的 AI API 以生成您指定的标签。因此，您将两全其美。不幸的是，它并不是完全免费的，但该计划对于少量图像来说成本相当低，并且您在开始时可以获得免费样本（而且培训过程也是免费的）。
免责声明：我是 vize.it 的共同创建者之一。
It's not entirely clear whether you would like to define the tags to use yourself, or just let the software use a "common sense" universal set of tags about the objects shown etc.
Let's say you want to define your own set of tags — they can be about the year season a photo was taken in, a mood associated with the image (based on color scheme and depicted objects etc.), or something technical you need to distinguish (nudity, detail, background type etc.).
We can use machine learning for this! It's a branch of artificial intelligence that learns rules (like how to tag images — even very complicated rules) when we give it many examples of the images. So the main step for you is to gather a set of example images for each tag you want.
Once you do this, for images you have two main options:
Use a deep learning framework which lets you apply neural networks on the problem. You will need to split your data to smaller parts, do quite a bit of coding and unless you have a lot of images, use a variety of tricks to get it learn your task well. Unless you are interested in research, caffe and TensorFlow are the ones to look at now (a year ago the recommendation was different, and a year from now it may be different again).
Use an online API, as you mention. But for the task where you want your own set of tasks, you don't have many options, as most services just do general classification - they sort your images based on what "daily life" objects they detect on the images (and sometimes special cases like NSFW, but often not on the sensitivity level you would like).
An option you have among web-based APIs is vize.it, which offers a web interface where you can upload and label your example images and it lets you train your own AI API which generates the tags you specified. So you are getting the best of both worlds. Unfortunately, it's not completely free, but the plan is fairly low-cost for small amount of images and you get a free sample at the begining (plus the training process is free too).
还有许多其他项目，如 imagga、alchemyapi、clevapi 等，使用 google 来查找更多内容，
如果您需要更多帮助，可以给我发短信。
try clarifai api the best api i have come across. also they offer 5000 image tags per month for free so you can test it. they offer few starter projects for Android, ios, javascript, python etc
there are many others like imagga, alchemyapi, clevapi etc use google to find more
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我认为，如果图像可以自动标记，那么 Google 早就放弃了图像标记器。不幸的是，计算机在理解图像方面存在很多困难。
编辑：
如果您对计算机视觉研究感兴趣，请查看CVPapers，特别是开源计算机视觉实现。自动图像标记还远未得到解决（除非您有一组非常具体/受限的主题）。
引自 Google 指南，2007 年 3 月 13 日星期二 ：
<块引用>
“拉里·佩奇”和“谢尔盖·布林”这两个词出现在埃里克·施密特的图像附近、图像标题或这些图像的链接中。谷歌猜测这些词与图像相关。 Google 技术尚未达到可以通过直接查看图像来判断图像内容的程度。
HTH，不要抱太大希望。
PS：我希望你（或其他人）证明我错了，并在这里与我分享;-)
编辑2：
我刚刚偶然发现了Voc 2010 Challenge，在我看来，它很好地说明了计算机视觉进展的当前状态。在其中一个挑战中，参赛者必须在图像中（从非常有限的一组对象中）找到一个对象并对其进行分类。在结果页面上您可以看到，其中一种算法能够以 93% 的准确率对飞机进行分类，但在其他类别中“失败”。
那只是为了寻找“事物”，甚至不是形容词或情感。
I think if images could be labeled automatically, Google would have abandoned the image labeler a long time ago. Unfortunately, computers have a lot of trouble understanding images.
Edit:
If you are interested in computer vision research have a look at CVPapers, especially Open Source Computer Vision Implementations. Automatic image labeling is far from being solved (unless you have a very specific/restricted set of topics).
Quote from The Google Guide from Tuesday March 13, 2007:
HTH, don't get your hopes up too high.
P.S.: I hope you (or someone else) proves me wrong and shares it here with me ;-)
Edit2:
I just stumbled across the Voc 2010 Challenge, which, in my opinion, illustrates very well the current state of computer vision advances. In one of the challenges the contestants have to find an object (from a very limited set of objects) in the image and classify it. On the result page you can see, that one of the algorithms manages to classify air plane with a 93% accuracy, but "fails" at other categories.
That is just for the quest to find the "things", not even adjectives or emotions.
查看 https://imagga.com/
它有一些令人印象深刻的结果。还有一些非常有趣的结果...值得庆幸的是，生成的所有标签都带有置信值，因此您始终可以忽略低于阈值的任何内容（对于我的用例，约为 15%）。
每月免费提供 12,000 张图片，不错。如果您有超过 12,000 个月份图像，则只需对您的请求进行排队即可。
Check out https://imagga.com/
It has some impressive results. Also some wildly entertaining results... Thankfully all tags generated come with a confidence value, so you could always ignore anything less than a threshold (~15% for my use case).
12,000 images a month for free, not bad. If you have over 12,000 month images then just queue your requests.
目前尚不完全清楚您是否想要定义标签以供自己使用，或者只是让软件使用关于所显示对象的“常识”通用标签集等假设
您想要定义自己的一组标签 - 它们可以与照片拍摄的季节、相关的心情有关。与图像（基于配色方案和描绘的对象等），或您需要区分的技术内容（裸体、细节、背景类型等）。
我们可以使用机器学习来实现这一点！它是人工智能的一个分支，当我们给它许多图像示例时，它会学习规则（例如如何标记图像，甚至是非常复杂的规则）。因此，您的主要步骤是为您想要的每个标签收集一组示例图像。
完成此操作后，对于图像，您有两个主要选择：
使用深度学习框架，它可以让您将神经网络应用于问题。您需要将数据分割成更小的部分，进行大量编码，除非您有大量图像，否则请使用各种技巧让它很好地学习您的任务。除非您对研究感兴趣，caffe 和 TensorFlow 是现在要考虑的（一年前的建议是不同的，一年后可能会再次不同）。
正如您提到的，使用在线 API。但对于您想要自己的一组任务的任务，您没有太多选择，因为大多数服务只是进行一般分类 - 它们根据检测到的“日常生活”对象对您的图像进行排序在图像上（有时是 NSFW 等特殊情况，但通常不是您想要的敏感度级别）。
基于网络的 API 中的一个选项是 vize.it，它提供了一个网络界面您可以在其中上传示例图像并为其添加标签，它可以让您训练自己的 AI API 以生成您指定的标签。因此，您将两全其美。不幸的是，它并不是完全免费的，但该计划对于少量图像来说成本相当低，并且您在开始时可以获得免费样本（而且培训过程也是免费的）。
免责声明：我是 vize.it 的共同创建者之一。
It's not entirely clear whether you would like to define the tags to use yourself, or just let the software use a "common sense" universal set of tags about the objects shown etc.
Let's say you want to define your own set of tags — they can be about the year season a photo was taken in, a mood associated with the image (based on color scheme and depicted objects etc.), or something technical you need to distinguish (nudity, detail, background type etc.).
We can use machine learning for this! It's a branch of artificial intelligence that learns rules (like how to tag images — even very complicated rules) when we give it many examples of the images. So the main step for you is to gather a set of example images for each tag you want.
Once you do this, for images you have two main options:
Use a deep learning framework which lets you apply neural networks on the problem. You will need to split your data to smaller parts, do quite a bit of coding and unless you have a lot of images, use a variety of tricks to get it learn your task well. Unless you are interested in research, caffe and TensorFlow are the ones to look at now (a year ago the recommendation was different, and a year from now it may be different again).
Use an online API, as you mention. But for the task where you want your own set of tasks, you don't have many options, as most services just do general classification - they sort your images based on what "daily life" objects they detect on the images (and sometimes special cases like NSFW, but often not on the sensitivity level you would like).
An option you have among web-based APIs is vize.it, which offers a web interface where you can upload and label your example images and it lets you train your own AI API which generates the tags you specified. So you are getting the best of both worlds. Unfortunately, it's not completely free, but the plan is fairly low-cost for small amount of images and you get a free sample at the begining (plus the training process is free too).
Disclaimer: I'm one of vize.it co-creators.
尝试 clarifai api，这是我遇到过的最好的 api。他们还每月免费提供 5000 个图像标签，以便您可以测试它。他们为 Android、ios、javascript、python 等提供了一些入门项目，
还有许多其他项目，如 imagga、alchemyapi、clevapi 等，使用 google 来查找更多内容，
如果您需要更多帮助，可以给我发短信。
try clarifai api the best api i have come across. also they offer 5000 image tags per month for free so you can test it. they offer few starter projects for Android, ios, javascript, python etc
there are many others like imagga, alchemyapi, clevapi etc use google to find more
if u need more help u can text me.