HOGgles: Visualizing Object Detection Features

Abstract

We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.

Approximative Coding Methods for Channel Representations

“…However, less than six iterations are required to obtain a valid density function. The use of a weighted norm (30) has a small impact on the final result, generating a solution slightly closer to the true distribution function in the highdensity areas in Fig. 4, top right. …”

“…Note that each element of the Jacobian requires numerical evaluation of an integral. The maximum approximative entropy approach uses Newton iterations for fulfilling the nonnegativity constraints and gradient descent for minimizing (30). The Jacobian is obtained by matrix computations, with the number of elements related to the number of channels used.…”

“…Similarly, SIFT descriptors can be considered as a particular variant of channel representation of local orientation and the latter framework allows generalizing to color images [7]. HOG descriptors are a specific variant of channel coded feature maps CCFMs [16], but in contrast to the former no additional visualization [30] is required. CCFMs …”

A hierarchical inferential method for indoor scene classification

“…The knowledge base is constructed from the training dataset with ProbCog (Hall et al, 2009). The method implemented by Vondrick et al (2013) was used to visualize the object detection results. Two-fold cross validation was used to compare our proposal with other methods, and the mean precision was reported.…”

Semantically-driven automatic creation of training sets for object recognition

“…Even though at a first glance one may think that what is being recognized is an object that is given "out there in the world", at a closest look one may see that what is detected and then assigned to a certain class of objects is something that is constructed out from an aggregation of features that a classifier has been trained to recognize as that particular kind of object [1]. As a consequence, the fact that a certain aggregation of features is recognized as a dog or as a building, strongly depends on the images that have been chosen to be part of the training set [2].…”