A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification

Munawar Hayat, Salman Khan, Mohammed Bennamoun, Senjian An

    Research output: Contribution to journalArticle

    26 Citations (Scopus)
    2 Downloads (Pure)

    Abstract

    Unlike standard object classification, where the image to be classified contains one or multiple instances of the same object, indoor scene classification is quite different since the image consists of multiple distinct objects. Furthermore, these objects can be of varying sizes and are present across numerous spatial locations in different layouts. For automatic indoor scene categorization, large-scale spatial layout deformations and scale variations are therefore two major challenges and the design of rich feature descriptors which are robust to these challenges is still an open problem. This paper introduces a new learnable feature descriptor called “spatial layout and scale invariant convolutional activations” to deal with these challenges. For this purpose, a new convolutional neural network architecture is designed which incorporates a novel “spatially unstructured” layer to introduce robustness against spatial layout deformations. To achieve scale invariance, we present a pyramidal image representation. For feasible training of the proposed network for images of indoor scenes, this paper proposes a methodology, which efficiently adapts a trained network model (on a large-scale data) for our task with only a limited amount of available training data. The efficacy of the proposed approach is demonstrated through extensive experiments on a number of data sets, including MIT-67, Scene-15, Sports-8, Graz-02, and NYU data sets
    Original languageEnglish
    Article number7539697
    Pages (from-to)4829-4841
    Number of pages13
    JournalIEEE Transactions on Image Processing
    Volume25
    Issue number10
    DOIs
    Publication statusPublished - 2016

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