Progress in Image Search Tech
April 8, 2015
Anyone interested in the mechanics behind image search should check out the description of PicSeer: Search Into Images from YangSky. The product write-up goes into surprising detail about what sets their “cognitive & semantic image search engine” apart, complete with comparative illustrations. The page’s translation seems to have been done either quickly or by machine, but don’t let the awkward wording in places put you off; there’s good information here. The text describes the competition’s approach:
“Today, the image searching experiences of all major commercial image search engines are embarrassing. This is because these image search engines are
- Using non-image correlations such as the image file names and the texts in the vicinity of the images to guess what are the images all about;
- Using low-level features, such as colors, textures and primary shapes, of image to make content-based indexing/retrievals.”
With the first approach, they note, trying to narrow the search terms is inefficient because the software is looking at metadata instead of inspecting the actual image; any narrowed search excludes many relevant entries. The second approach above simply does not consider enough information about images to return the most relevant, and only most relevant, results. The write-up goes on to explain what makes their product different, using for their example an endearing image of a smiling young boy:
“How can PicSeer have this kind of understanding towards images? The Physical Linguistic Vision Technologies have can represent cognitive features into nouns and verbs called computational nouns and computational verbs, respectively. In this case, the image of the boy is represented as a computational noun ‘boy’ and the facial expression of the boy is represented by a computational verb ‘smile’. All these steps are done by the computer itself automatically.”
See the write-up for many more details, including examples of how Google handles the “boy smiles” query. (Be warned– there’s a very brief section about porn filtering that includes a couple censored screenshots and adult keyword examples.) It looks like image search technology progressing apace.
Cynthia Murrell, April 08, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
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