Using global bag of features models in random fields for joint categorization and segmentation of objects

Singaraju, Dheeraj; Vidal, René

doi:10.1109/cvpr.2011.5995469

Cited by 26 publications

(37 citation statements)

References 21 publications

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“…Saverese et al [4] propose a model which instead characterizes the relative locations. Motivated by earlier work [5] on using correlograms of quantized colors for indexing and classifying images [5], [6], they use correlograms of visual words to model the spatial correlations between quantized local descriptors. The correlograms are three dimensional structures which in essence record the number of times two visual words appear at a particular distance from each other.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Density Estimation by a Weighted Bag of Visual Words Model

Zhang¹,

Zhang²

2015

IJMLC

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Abstract-Pedestrian density estimation is very useful and important under transportation environment. In this paper, we present a novel weighting scheme of "bag of visual words model" for pedestrian density estimation, which characterizes both the weight and the relative spatial arrangement aspects of all visual words in depicting an image. We firstly analyze the visual words generation process. By counting the number of images through which each visual word is clustered and computing the cluster radius of each visual word, we can give each visual word a weight. Specially, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an image. The representation captures both the absolute and relative spatial arrangement of the words and, through the choice and combination of the predicates, can characterize a variety of spatial relationships. We validate this hypothesis using a challenging ground truth pedestrian dataset. Our approach is shown to result in higher classification accuracy rates than a non-weighting bag-of-visual-words approach. The time used to generate the visual words of our approach is only 1/20 to 1/30 compared to the time of the traditional image feature cluster process.

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Section: Related Workmentioning

confidence: 99%

Pedestrian Density Estimation by a Weighted Bag of Visual Words Model

Zhang¹,

Zhang²

2015

IJMLC

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“…For example, Lee et al [21] introduced an object-graph descriptor for discovery of unknown object categories, which encode the object-level co-occurrence patterns; Singaraju et al [27] developed a random field model for joint categorization and segmentation of objects, where they introduced the higher order potentials that encode the classification cost of a histogram extracted from objects belonged to different categories; Jain et al [28] proposed a latent CRF model which captures the relations between features and visual words, relations between visual words and object categories, and spatial relations between visual words; Angin et al [29] proposed a simple iterative algorithm for object categorization by exploiting the global co-occurrence frequencies of objects; Sun T et al [30] proposed a object categorization method by combining local feature context with SVM classifiers.…”

Section: Related Workmentioning

confidence: 99%

Contextual object categorisation with energy‐based model

Ri¹,

DuHo²,

Choe³

et al. 2017

J. eng.

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：Object categorization is a hot issue of an image mining. Contextual information between objects is one of the important semantic knowledge of an image. However, the previous researches for an object categorization have not made full use of the contextual information, especially the spatial relations between objects. In addition, the object categorization methods, which generally use the probabilistic graphical models to implement the incorporation of contextual information with appearance of objects, are almost inevitable to evaluate the intractable partition function for normalization. In this work, we introduced fully-connected fuzzy spatial relations including directional, distance and topological relations between object regions, so the spatial relational information could be fully utilized. Then, the spatial relations were considered as well as co-occurrence and appearance of objects by using energy-based model, where the energy function was defined as the region-object association potential and the configuration potential of objects. Minimizing the energy function of whole image arrangement, we obtained the optimal label set about the image regions and addressed the evaluation of intractable partition function in conditional random fields. Experimental results show the validity and reliability of this proposed method.

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“…Recent findings show that both segmentation and categorization significantly improve the performance of each other [22,24,31]. In this paper, we consider the interactions of these two tasks and propose an algorithm for joint object segmentation and categorization.…”

Section: Introductionmentioning

confidence: 99%

Decomposed Learning for Joint Segmentation and Categorization

Tsai

Yang

2013

Procedings of the British Machine Vision Conference 2013

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We present a learning algorithm for joint object segmentation and categorization that decomposes the original problem into two sub-tasks and admits their bidirectional interaction. In the first stage, in order to decompose output space, we train category-specific segmentation models to generate figure-ground hypotheses. In the second stage, by taking advantage of object figure-ground information, we train a multi-class segment-based categorization model to determine the object class. A re-ranking strategy is then applied to classified segments to obtain the final category-level segmentation results. Experiments on the Graz-02 and Caltech-101 datasets show that the proposed algorithm performs favorably against the state-of-the-art methods.

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Using global bag of features models in random fields for joint categorization and segmentation of objects

Cited by 26 publications

References 21 publications

Pedestrian Density Estimation by a Weighted Bag of Visual Words Model

Pedestrian Density Estimation by a Weighted Bag of Visual Words Model

Contextual object categorisation with energy‐based model

Decomposed Learning for Joint Segmentation and Categorization

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