A novel cognition-inspired, agnostic framework is proposed for building maps in mobile robotics that are efficient in terms of image matching retrieval for solving Visual Place Recognition (VPR) problem. A dataset, 'ESSEX3IN1', is also presented to demonstrate the significantly enhanced performance of state-of-the-art VPR techniques when combined with the proposed framework.
Different research works have tried overcoming these intrinsic limitations of frame-cum-Place sampling by introducing Visual-distinctiveness based selection. Authors in @cite_6 use a custom-designed algorithm that detects change-point for segmentation between different topological places in both indoor and outdoor scenes. Image-sequence Partitioning for creating sparse topological maps is presented in @cite_13 , where sequences of images are partitioned into nodes places using four descriptors namely GIST, Optical Flow, Local Feature Mapping and Common-Important Words. In @cite_24 , a thematic approach is adapted to evaluate the novelty of an incoming image by co-relating it with the redundancy of visual features topics. Bayesian Surprise is adapted with immunity to sensor type, for extracting landmarks to create a sparse topological map in @cite_8 . Online Topic Modelling with visual surprise calculation is done in @cite_12 for under-water robotic explorations. An incremental unsupervised Place discovery scheme is adopted in @cite_9 which fuses information over time to find visually distinct places.
@cite_8 @cite_9 @cite_6 @cite_24 @cite_13 @cite_12
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This paper presents an approach for human action recognition by finding the discriminative key frames from a video sequence and representing them with the distribution of local motion features and their spatiotemporal arrangements. In this approach, the key frames of the video sequence are selected by their discriminative power and represented by the local motion features detected in them and integrated from their temporal neighbors. In the key frame’s representation, the spatial arrangements of the motion features are captured in a hierarchical spatial pyramid structure. By using frame by frame voting for the recognition, experiments have demonstrated improved performances over most of the other known methods on the popular benchmark data sets. Recognizinghumanactionfromimagesequencesis an appealingyet challengingproblem in computer vision with many applications including motion capture, human-computer interaction, environment control, and security surveillance. In this paper, we focus on recognizing the activities of a person in an image sequence from local motion features and their spatiotemporal arrangements. Our approach is motivated by the recent success of “bag-of-words” model for general object recognition in computer vision[21, 14]. This representation, which is adapted from the text retrieval literature, models the object by the distribution of words from a fixed visual code book, which is usually obtained by vector quantization of local image visual features. However, this method discards the spatial and the temporal relations among these visual features, which could be helpful in object recognition. Addressing this problem, our approach uses a hierarchical representation for the key frames of a given video sequence to integrate information from both the spatial and the temporal domains. We first apply a spatiotemporal feature detector to the video sequence and obtain the local motion features. Then we generate a visual word code book by quantization of the local motion features and assign a word label to each of them. Next we select key frames of the video sequence by their discriminative power. Then, for each key frame, we integrate the visual words from its nearby frames, divide the key frame spatially into finer subdivisions and compute in each cell the histograms of the visual words detected in this key frame and its temporal neighbors. Finally, we concatenate the histograms from all cells and use This paper describes an online place discovery and recognition engine that fuses information over time to create topologically distinct places. A key motivation is the recognition that a single image may be a poor exemplar of what constitutes a place. Images are not ‘places’ nor are they ‘documents’. Instead, by treating image-sequences as a multimodal distribution over topics – and by discovering topics incrementally and online – it is possible to both reduce the memory footprint of place recognition systems, and to improve precision and recall. Distinctive key-places are represented by a cluster topics found from the covisibility graph of a relative simultaneous localization and mapping engine – key-places inherently span many images. A dynamic vocabulary of visual words and density based clustering is used to continually estimate a set of visual topics, changes in which drive the placerecognition process. The system is evaluated using an indoor robot sequence, a standard outdoor robot sequence and a longterm sequence from a static camera. Experiments demonstrate qualitatively distinct themes associated with discovered places – from common place types such as ‘hallway’, or ‘desk-area’, to temporal concepts such as ‘dusk’, ‘dawn’ or ‘mid-day’. Compared to traditional image-based place-recognition, this reduces the information that must be stored without reducing place-recognition performance. This paper addresses the problem of detecting salient areas within natural images. We shall mainly study the problem under unsupervised setting, i.e., saliency detection without learning from labeled images. A solution of multitask sparsity pursuit is proposed to integrate multiple types of features for detecting saliency collaboratively. Given an image described by multiple features, its saliency map is inferred by seeking the consistently sparse elements from the joint decompositions of multiple-feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a constrained nuclear norm and as an l2,1 -norm minimization problem, which is convex and can be solved efficiently with an augmented Lagrange multiplier method. Compared with previous methods, which usually make use of multiple features by combining the saliency maps obtained from individual features, the proposed method seamlessly integrates multiple features to produce jointly the saliency map with a single inference step and thus produces more accurate and reliable results. In addition to the unsupervised setting, the proposed method can be also generalized to incorporate the top-down priors obtained from supervised environment. Extensive experiments well validate its superiority over other state-of-the-art methods. We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many visual categories and instances. Our approach is based on a parametric figureground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image followed by a sequential inference algorithm that produces a complete scene estimate. Our contributions can be summarized as follows: (1) a generalization of parametric max flow figure-ground proposal methodology to take advantage of intensity and depth information, in order to systematically and efficiently generate the breakpoints of an underlying spatial model in polynomial time, (2) new region description methods based on second-order pooling over multiple features constructed using both intensity and depth channels, (3) a principled search-based structured prediction inference and learning process that resolves conflicts in overlapping spatial partitions and selects regions sequentially towards complete scene estimates, and (4) extensive evaluation of the impact of depth, as well as the effectiveness of a large number of descriptors, both pre-designed and automatically obtained using deep learning, in a difficult RGB-D semantic segmentation problem with 92 classes. We report state of the art results in the challenging NYU Depth Dataset V2 [44], extended for the RMRC 2013 and RMRC 2014 Indoor Segmentation Challenges, where currently the proposed model ranks first. Moreover, we show that by combining second-order and deep learning features, over 15 relative accuracy improvements can be additionally achieved. In a scene classification benchmark, our methodology further improves the state of the art by 24 .