Computer Vision Papers at NIPS 2009

Here I some computer vision papers I found interesting at NIPS 2009 in Vancouver.

[pdf][bib] Unsupervised Detection of Regions of Interest Using Iterative Link Analysis (NIPS 2009)
Gunhee Kim, Antonio Torralba

[pdf][bib] Region-based Segmentation and Object Detection (NIPS 2009)
Stephen Gould, Tianshi Gao, Daphne Koller

[pdf][bib] Segmenting Scenes by Matching Image Composites (NIPS 2009)
Bryan Russell, Alyosha Efros, Josef Sivic, Bill Freeman, Andrew Zisserman

[pdf][bib] Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships (NIPS 2009)
Tomasz Malisiewicz, Alyosha Efros

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.

Exemplars, Prototypes, and towards a Theory of Concepts for AI

While initial musings (and some early theories) on Categorization come from Philosophy (think Categories by Aristotle), most modern research on Categorization which adheres to the scientific method comes from Psychology (Concept Learning on Wikipedia). Two popular models which originate from Psychology literature are Prototype Theory and Exemplar Theory. Summarizing briefly, categories in Prototype Theory are abstractions which summarize a category while categories in Exemplar Theory are represented nonparametrically. While I'm personally a big proponent of Exemplar Theory (see my Recognition by Association CVPR2008 paper), I'm not going to discuss the details of my philosophical stance in this post. I want to briefly point out the shortcomings of these two simplified views of concepts.

Researchers focusing on Categorization are generally dealing with a very simplified (and overly academic) view of the world -- where the task is to categorize a single input stimulus. The problem is that if we want a Theory of Concepts that will be the backbone of intelligent agents, we have to deal with relationships between concepts with as much fervor as the representations of concepts themselves. While the debate concerning exemplars vs. prototypes has been restricted to these single stimulus categorization experiments, it is not clear to me why we should prematurely adhere to one of these polarized views before we consider how we can make sense of inter-category relationships. In other words, if an exemplar-based view of concepts looks good (so-far) yet it is not as useful for modeling relationships as a prototype-view, then we have to change our views. Following James' pragmatic method, we should evaluate category representations with respect to a larger system embodied in an intelligent agent (and its ability to cope with the world) and not the overly academic single-stimulus experiments dominating experimental psychology.

On another note, I submitted my most recent research to NIPS last week (supersecret for now), and went to a few Phish concerts. I'm driving to California next week and I start at Google at the end of June. I also started reading a book on James and Wittgenstein.