A Hash Table Approach for Large Scale Perceptual Anchoring

Abstract

Abstract-Perceptual anchoring deals with the problem of creating and maintaining the connection between percepts and symbols that refer to the same physical object. When approaching long term use of an anchoring framework which must cope with large sets of data, it is challenging to both efficiently and accurately anchor objects. An approach to address this problem is through visual perception and computationally efficient binary visual features. In this paper, we present a novel hash table algorithm derived from summarized binary visual features. This algorithm is later contextualized in an anchoring framework. Advantages of the internal structure of proposed hash tables are presented, as well as improvements through the use of hierarchies structured by semantic knowledge. Through evaluation on a larger set of data, we show that our approach is appropriate for efficient bottom-up anchoring, and performance-wise comparable to recently presented search tree algorithm.

Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision

“…The principal idea behind the proposed approach is to maintain a byte frequency count for each byte and for each binary string of a descriptor set. Specifically, the approach transforms a binary descriptor set into a histogram that maintains the byte value distribution of the binary strings, which constitute a descriptor set [17]. The inspiration for this approach stems from two core questions: 1) is it possible to reduce the total search space prior to matching individual descriptor strings?…”

I Would Like Some Food: Anchoring Objects to Semantic Web Information in Human-Robot Dialogue Interactions

“…However, for the visual features, a more complex method to extract the predicate grounding relation, based on existing image databases was used. This method is outside the scope of this paper but a description can be found in [11].…”

“…The system receives a percept and invokes a matching algorithm in which the percept is compared against all previously stored anchors in the anchoring system. The details of the match algorithm can be found in [11]. Depending on the results of matching, anchors are either created or maintained through two functionalities:…”