Confidence-based Cue Integration for Visual Place Recognition

A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a given task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents a recognition algorithm able to measure its own level of confidence and, in case of uncertainty, to seek for extra information so to increase its own knowledge and ultimately achieve better performance. We focus on the visual place recognition problem for topological localization, and we take an SVM approach. We propose a new method for measuring the confidence level of the classification output, based on the distance of a test image and the average distance of training vectors. This method is combined with a discriminative accumulation scheme for cue integration. We show with extensive experiments that the resulting algorithm achieves better performances for two visual cues than the classic single cue SVM on the same task, while minimising the computational load. More important, our method provides a reliable measure of the level of confidence of the decision

The more you learn, the less you store: memory\--controlled incremental SVM

The capability to learn from experience is a key property for a visual recognition algorithm working in realistic settings. This paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs with a method reducing the number of support vectors needed to build the decision function without any loss in performance, introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is guaranteed to achieve the same recognition results as the original incremental method while reducing the memory growth. Moreover, experiments in two domains of material and place recognition show the possibility of a consistent reduction of memory requirements with only a moderate loss in performance. For example, results show that when the user accepts a reduction in recognition rate of 5%, this yields a memory reduction of up to 50%

The More you Learn, the Less you Store: Memory-Controlled Incremental SVM

This paper presents a novel SVM-based algorithm for visual object recognition, capable of learning model representations incrementally. We combine an incremental extension of SVMs with a method which reduces the number of support vectors needed to build the decision function without any loss in performance. The resulting algorithm is guaranteed to achieve the same recognition performance as the original incremental method while reducing the memory requirements. We benchmarked the novel technique against the batch method and the original version of incremental SVM. Experiments were performed in two domains, material categorization and indoor place recognition. In both applications, results show that the two incremental methods preserve the performance of the batch algorithm, but only our new technique consistently achieves a statistically significant reduction of the memory requirements. We then propose an extension to the part of the algorithm controlling the number of support vectors to be stored. We introduce a parameter which permits a user-set trade-off between performance and memory reduction. This property is potentially useful in applications like indoor place recognition for multi-sensory topological mapping, where the memory size of the visual models must be kept under control

SVM-based Transfer of Visual Knowledge Across Robotic Platforms

This paper presents an SVM-based algorithm for the transfer of knowledge across robot platforms aiming to perform the same task. Our method exploits efficiently the transferred knowledge while updating incrementally the internal representation as new information is available. The algorithm is adaptive and tends to privilege new data when building the SV solution. This prevents the old knowledge to nest into the model and eventually become a possible source of misleading information. We tested our approach in the domain of vision-based place recognition. Extensive experiments show that using transferred knowledge clearly pays off in terms of performance and stability of the solution

The More you Learn, the Less you Store: Memory-controlled Incremental SVM for Visual Place Recognition

The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs with a method reducing the number of support vectors needed to build the decision function without any loss in performance introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach

Incremental Learning for Place Recognition in Dynamic Environments

Vision\--based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, a visual recognition algorithm should have two key properties: robustness and adaptability. This paper focuses on the latter, and presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the fixed\--partition incremental SVM, which allows to control the memory requirements as the system updates its internal representation. At the same time, it preserves the recognition performance of the batch algorithm and runs online. In order to assess the method, we acquired a database capturing the intrinsic variability of places over time. Extensive experiments show the power and the potential of the approach