The best method for what you ask is using local features detectors like OpenCV's SIFT, SURF and ORB, for example.
You need at least one picture from the object you want to detect. Afterwards, those algorithms can compare that image with other images to see if they are similar enough.
Here is the Documentation for the algorithms.
http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html
- SURF and SIFT ('nonfree'):
http://docs.opencv.org/modules/nonfree/doc/feature_detection.html
The way these algorithms work for that task is by selecting interesting points for each image, and compare them to see if they match. If several matches are found, it is most likely the images have the same object.
Tutorials (from Feature Detection and below):
http://docs.opencv.org/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.html
You can also find C++ samples related to this topic here (samples are also within OpenCV download package):
- eg. "matching_to_many_images.cpp"
- "video_homography.cpp"
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/cpp
And Android Java samples here (unrelated but also helpful):
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/android
Or Python samples which are actually the more updated ones for this topic (at the time this post was written):
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/python2
As a final note, like @BDFun said in the comment, this is not trivial to do.
More - if you want an overview of OpenCV Feature detection and description, check this post.