Paper of the day: Object Detection with Grammar Models. Ross Girshick, Pedro Felzenszwalb, David McAllester. NIPS 2011.
The paper describes an approach using formal grammars applied to object recognition — and while the idea of using grammars for computer vision has a long history — an approach that achieves compelling results on PASCAL (specifically the person category) is quite new. The work is essentially an extension of Deformable Part Models (DPM) that tries to formalize the notion of objects, parts, mixture models, etc., by defining a formal grammar for objects. The grammar allows for such things as optional parts, mixtures at the part levels, explicit occlusion reasoning and recursive parts (parts of parts). The grammar model is defined by hand, while learning of all parameters (HOG template appearances, deformation parameters, scores of productions) is performed using latent SVMs (an extension of what was done for DPMs).
The NIPS paper is quite dense. To understand the model better I found it useful to first briefly skim the Wikipedia entry on Context Free Grammars first and to read the earlier tech report that goes into far more detail on defining the model itself. Online talks by Ross (link), Pedro (link) and David (link) are also helpful.
The particular grammar used achieves 49.5 AP on the person category in PASCAL with contextual reasoning. For comparison, DPMs in their latest incarnation achieved 44.4 (in their raw form) and poselets (the state-of-the-art algorithm on this data) had 48.5 AP. My impression is that the largest gains come from the fact that the model performs explicit occlusion reasoning — which is very cool — and hence does not require a mixture model at the object level or bounding box prediction. Some example results:
The general idea behind this work is quite interesting. My impression though is that making object grammars work in practice for a wide range of categories has major limitations. I see three issues: (1) the model must be defined by hand for each category, (2) initialization is likely crucial and obtaining a good initialization is not trivial (for the person category there was a trick the authors were able to use for initialization described in S4.3), and (3) performance seems to be quite sensitive to the model used. In David’s talk, David discusses how they tried typing in numerous grammars for person and it was finally the occlusion style model that really helped. I suspect that if the grammars worked for a wider range of models we would have seen results on all of PASCAL.
Regardless, the paper provides great food for thought and suggests future directions where DPMs can go.
[This is a followup post to my original post on Superpixels.]
Today’s “paper of the day”: M. Van den Bergh, X. Boix, G. Roig, B. de Capitani and Luc Van Gool, SEEDS: Superpixels Extracted via Energy-Driven Sampling, ECCV 12. Website (with code).
The paper describes a very fast algorithm — “SEEDS” — for generating superpixels. SEEDS starts by partitioning an image into a grid of square superpixels, and then refines each superpixel by shifting pixels (or blocks of pixels) at boundaries from one superpixel to another. What’s cool about this approach is that it can be made very fast — each update is local and only requires very simple comparisons to reach a decision about transferring pixel ownership. Moreover, the algorithm can be stopped at any point in time (this is how the authors are able to achieve 30Hz processing time — running the algorithm longer results in better segmentations). While the presentation of the paper has some issues (convoluted descriptions, overly complex notation, tiny unreadable figures), the algorithm itself is very simple, fast, and effective. Good stuff.
Some superpixel results (taken from author website):
The quality of the superpixel segmentations is quite high according to the standard error metrics for superpixels (see this paper for an excellent description of the error metrics). In fact, the quality of the superpixels is much higher than that of SLIC, the subject of my previous post. One issue I realized about the SLIC paper is that it presented and compared results at only a single setting of the number of superpixels — the SEEDS paper does a much better job of evaluating performance as a function of the number of superpixels.
Overall, I’m a big fan of the work on superpixels. In my opinion segmentation has received a disproportionate amount of attention in the literature — but really superpixels are much more useful (and more commonly used) than complete segmentations. Superpixels are an acknowledgement that segmentation is not an end in itself but rather as a pre-processing step for further computer vision.
What I’d really like to see is superpixel algorithms that can achieve extremely high boundary recall (currently boundary recall is at around 90% for 600 superpixels or so, although the localization of the edges is a bit rough). While of course this is THE goal of superpixel algorithms, it’s surprising to me that achieving higher recall is as challenging as it is. Segmentation is notoriously difficult — but with superpixels one can cheat by being very liberal at reporting edges. I’d be curious to see an analysis of what edges are still being missed. While there’s some edges that have no gradient (illusory contours), this does not seem to be the dominant issue.. So, what would it take to get higher boundary recall?
SLIC Superpixels Compared to State-of-the-art Superpixel Methods, Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk. PAMI12. Website.
Achanta et al. analyze the requirement for superpixels, provide reasonable targets and performance measures, and compare a few of the leading approaches for superpixels (including Felzenszwalb and Huttenlocher’s approach and normalized cuts from Berkeley). They propose a new approach termed “simple linear iterative clustering” (SLIC). SLIC starts by defining cluster centers essentially in a grid over the image (with some smart initialization), assigning all remaining pixels to one of the cluster centers, recomputing the centers, and iterating. L2 distance over color and position is used to assign pixels to clusters. Only nearby cluster centers are checked for each pixel to keep things efficient.. I’m omitting some details, but that’s the gist of the algorithm. Couldn’t ask for anything simpler!
The result is a very fast algorithm (fraction of a second per image) and results are visually pleasing. Examples of superpixels at different scales (taken from author website):
The superpixels are fairly regular, compact, and provide good boundary recall (qualitative results are provided). Essentially all you could ask for from superpixels!
Source code (GPL unfortunately — people please stop using GPL so code can be used by all — consider BSD or LGPL) is provided on the project website. This is definitely THE algorithm I’ll use if ever I use superpixels in my code. Nice.
A great example of a simple algorithm that outperforms much more complex and computationally expensive approaches. (Note: similar ideas were proposed by my colleagues Larry Zitnick and Sing Bing Kang in their IJCV07 paper as the authors note). These ideas weren’t presented at a computer vision conference (indeed papers of this form often have trouble getting into CV conferences due to some misguided notion of “novelty”) — which is unfortunate because it may not get as much exposure as it deserves.
Going home tonight and will be thinking about applications for superpixels in sliding window object detection…