I, for one, welcome our new Visual Memex-based overlords

Welcome to the era of visual intelligence -- the era of Visual Memex-based overlords (now in 3D!)
  

Image from  Context-Based Search for 3D Models, by Matthew Fisher and Pat Hanrahan

The goal of today's post is simple: to empower you, the reader, with an exciting and fresh perspective on the problem of visual reasoning.  This simple idea is one of the central tenets promulgated in my upcoming doctoral dissertation -- and but I'd like to give this potent meme a head start.  Visual Memex-style reasoning is not the kind of reasoning that is described in classic graduate level textbooks on AI (e.g. first-order logic).  In the case that you've mentally over-fit to a graduate-level CS curriculum, you might even portray my iconoclastic views as ramblings of a lunatic -- this is okay, I know at least Ludwig would be proud.

The Visual Memex is a mentality/perspective which, I believe, can overcome many limitations faced by modern computer vision systems.  What the Visual Memex can do for visual intelligence is akin to what the World Wide Web has done for knowledge (see Weinberger's excellent book "Everything is Miscellaneous" for the full argument).  It's akin to using Google for acquiring knowledge instead of going to the library -- maybe knowledge was never meant to be embedded in bookshelves.  The idea is embarrassingly simple: replace visual object categories with object exemplars and relationships between those exemplars.  Maybe the linguistic categories that we (as humans) cannot seem to live without are mere shadows cast on the wall of a dark cave.  Psychologists have long abandoned rigid categories in their models of how humans think about concepts, but the notion of a class is so fundamental to contemporary Machine Learning that many haven't even bothered to question its tenuous foundations.  While categories (also referred to as classes) definitely make learning algorithms easier to formalize, maybe its better to let the data speak for itself.  Free the data!

One upcoming research paper inspired by this category-free mentality is: Context-Based Search for 3D Models, by Matthew Fisher and Pat Hanrahan, of Stanford University.  This paper will be presented at SIGGRAPH Asia 2010.  Maybe it is time to abandon those rigid categories and memexify your own research problem?

Further reading:

• The Classic: Vannevar Bush's Memex
• Plato's Allegory of the Cave
• 2D object recognition: Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships
• 3D object modeling: Context-Based Search for 3D Models

The Visual Memex: Visual Object Recognition Without Categories

Figure 1

I have discussed the limitations of using rigid object categories in computer vision, and my CVPR 2008 work on Recognition as Association was a move towards developing a category-free model of objects. I was primarily concerned with local object recognition where the recognition problem was driven by the appearance/shape/texture features derived from within a segment (a region extraction from an image using an image segmentation algorithm). Recognition of objects was done locally and independently per region, since I did not have good model of category-free context at that time. I've given the problem of contextual object reasoning much thought over the past several years, and equipped with the power of graphical models and learning algorithms I now present a model for category-free object relationship reasoning.

Now its 2009, and its no surprise that I have a paper on context. Context is the new beast and all the cool kids are using it for scene understanding; however, categories are used so often for this problem that their use is rarely questioned. In my NIPS 2009 paper, I present a category-free model of object relationships and address the problem of context-only recognition where the goal is to recognize an object solely based on contextual cues. Figure 1 shows an example of such a prediction task. Given K objects and their spatial configuration, is it possible to predict the appearance of a hidden object at some spatial location?

Figure 2

I present a model called the Visual Memex (visualized in Figure 2), which is a non-parametric graph-based model of visual concepts and their interactions. Unlike traditional approaches to object-object modeling which learn potentials between every pair of categories (the number of such pairs scales quadratically with the number of categories), I make no category assumptions for context.

The official paper is out, and can be found on my project page:

Tomasz Malisiewicz, Alexei A. Efros. Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships. In NIPS, December 2009. PDF

Abstract: The use of context is critical for scene understanding in computer vision, where the recognition of an object is driven by both local appearance and the object's relationship to other elements of the scene (context). Most current approaches rely on modeling the relationships between object categories as a source of context. In this paper we seek to move beyond categories to provide a richer appearance-based model of context. We present an exemplar-based model of objects and their relationships, the Visual Memex, that encodes both local appearance and 2D spatial context between object instances. We evaluate our model on Torralba's proposed Context Challenge against a baseline category-based system. Our experiments suggest that moving beyond categories for context modeling appears to be quite beneficial, and may be the critical missing ingredient in scene understanding systems.

I gave at talk about my work yesterday at CMU's Misc-read and received some good feedback. I'll be at NIPS this December representing this body of research.

Bay Area Vision Meeting (BAVM 2009): Image and Video Understanding

Tomorrow (Friday) afternoon is BAVM 2009, a Bay Area workshop on Image and Video Understanding, which will be held at Stanford this year. It is being organized by Juan Carlos Niebles, one of Fei-Fei Li's students, and I will be there representing CMU. I have a poster about some new research and getting feedback is always good, but I'm really excited about meeting some of the other graduate students who work on image understanding. The Berkeley group has been pushing hard segmentation-driven image understanding so seeing what they're up to should be interesting. There will also be many fellow Googlers and researchers from companies in the Bay Area so it will also be a good place to network.

I look forward to hearing the invited speakers and the seeing the bleeding-edge stuff during the poster sessions. I'll try to blog a little bit about some of the coolest stuff I encounter when I get back.

Context is the 'glue' that binds objects in coherent scenes.

This is not my own quote. It is on of my favorites from Moshe Bar. It comes from his paper "Visual Objects in Context."

I have been recently giving context (in the domain of scene understanding) some heavy thought.
While Bar's paper is good, the one I wanted to focus on goes back to the 1980s. According to Bar, the following paper (which I wish everybody would at least skim) is "a seminal study that characterizes the rules that govern a scene's structure and their influence on perception."

Biederman, I., Mezzanotte, R. J. & Rabinowitz, J. C. Scene perception: detecting and judging objects undergoing relational violations. Cogn. Psychol. 14, 143–177 (1982).

Biederman outlines 5 object relations. The three semantic (related to object categories) relations are probability, position, and familiar size. The two syntactic (not operating at the object category level) relations are interposition and support. According to Biederman, "these relations might constitute a sufficient set with which to characterize the organizations of a real-world scene as distinct from a display of unrelated objects." The world has structure and characterizing this structure in terms of such rules is quite a noble effort.

A very interesting question that Biederman addresses is the following: do humans reason about syntactic relations before semantic relations or the other way around? A Gibsonian (direct perception) kind of way to think about the world is that processing of depth and space precedes the assignment of identity to the stuff that occupies the empty space around us. J.J. Gibson's view is in accordance with Marr's pipeline.

However, Biederman's study with human subjects (he is a psychologist) suggests that information about semantic relationships among objects is not accessed after semantic-free physical relationships. Quoting him directly, "Instead of a 3D parse being the initial step, the pattern recognition of the contours and the access to semantic relations appear to be the primary stages" as well as "further evidence that an object's semantic relations to other objects are processed simultaneously with its own identification."

Now that I've wet your appetite, let's bring out the glue.

P.S. Moshe Bar was a student of I. Biederman and S. Ullman (Against Direct Perception author).