If we set aside our academic endeavors of publishing computer vision and machine learning papers and sincerely ask ourselves, "What is the purpose of recognition?" a very different story emerges.
Let me first outline the contemporary stance on recognition (that is object recognition as is embraced by the computer vision community), which is actually a bit of a "non-stance" because many people working on recognition haven't bothered to understand the motivations, implications, and philosophical foundations of their work. The standard view of recognition is that it is equivalent to categorization -- assigning an object its "correct" category is the goal of recognition. Object recognition, as is found in vision papers, is commonly presented as single image recognition task which is not tied to an active and mobile agent that must understand and act in an environment around them. These contrived tasks are partially to blame for making us think that categories are the ultimate truth. Of course, once we've pinpointed the correct category we can look up information about the object category at hand in some sort of grand encyclopedia. For example, once we've categorized an object as a bird we can simply recall the fact that "it flies" from such a source of knowledge.
Most object recognition research is concerned with object representations (what features to compute from an image) as well as supervised (and semi-supervised) machine learning techniques to learn object models from data in order to discriminate and thus "recognize" object categories. The reason why object recognition has become so popular in the recent decade is that many researchers in AI/Robotics envision a successful vision system as a key component in any real-world robotic platform. If you ask a human to describe their environment, we will probably use a bunch of nouns to enumerate the stuff around them, so surely nouns must be the basic building blocks of reality! In this post I want to question this commonsense assumption that categories are the building blocks of reality and propose a different way of coping with reality, one that doesn't try to directly estimate a category from visual data.
I argue that just because nouns (and the categories they refer to) are the basis of effability for humans, it doesn't mean that nouns and categories are the quarks and gluons of recognition. Language is a relatively recent phenomenon for humans (think evolutionary scale here), and it is absent in many animals inhabiting the earth beside us. It is absurd to think that animals do not possess a faculty for recognition just because they do not have a language. Since animals can quite effectively cope with the world around them, there must be hope for understanding recognition in a way that doesn't invoke linguistic concepts.
Let me make my first disclaimer. I am not against categories altogether -- they have their place. The goal of language is human-human communication and intelligent robotic agents will inevitably have to map their internal modes of representation onto human language if we are to understand and deal with such artificial beings. I just want to criticize the idea that categories are found deep within our (human) neural architecture and serve as the basis for recognition.
Imagine a caveman and his daily life which requires quite a bit of "recognition"-abilities to cope with the world around him. He must differentiate pernicious animals from edible ones, distinguish contentious cavefolk from his peaceful companions, and reason about the plethora of plants around him. For each object that he recognizes, he must be able to determine whether it is edible, dangerous, poisonous, tasty, heavy, warm, etc. In short, recognition amounts to predicting a set of attributes associated with an object. Recognition is the linking of perceptible attributes (it is green and the size of my fist) to our past experiences and predicting attributes that are not conveyed by mere appearance. If we see a tiger, it is solely on our past experiences that we can call it dangerous.
So imagine a vector space, where each dimension encodes an attribute such as edible, throwable, tasty, poisonous, kind, etc. Each object can be represented as a point in this attribute space. It is language that gives us categories as a shorthand to talk about commonly found objects. Different cultures would give rise to different ways of cutting up the world, and this is consistent with what has been observed by psychologists. Viewing categories as a way of compressing attribute vectors not only makes sense but is in agreement with the idea that categories culturally arose much later than the ability for humans to recognize objects. Thus it makes sense to think of category-free recognition. Since a robotic agent who was programmed to think of the world in terms of categories will have to unroll categories to understand objects in terms of tangible properties if they are to make sense of the world around them, why not use the properties/attributes as the primary elements of recognition in the first place!?
These ideas are not entirely new. In Computer Vision, there is a CVPR 2009 paper Describing objects by their attributes by Farhadi, Endres, Hoiem, and Forsyth (from UIUC) which strives to understand objects directly using the ideas discussed above. In the domain of thought recognition, the paper Zero-Shot Learning with Semantic Output Codes by Palatucci, Pomerleau, Hinton, and Mitchell strives to understand concepts in a similar semantic basis.
I believe the field of computer vision has been conceptually stuck and the vehement reliance on rigid object categories is partially to blame. We should read more Wittgenstein and focus more on understanding vision as a mere component of artificial intelligence. If we play the recognize objects in a static image game (as Computer Vision is doing!) then we obtain a fragmented view of reality and cannot fully understand the relationship between recognition and intelligence.