Semantic scene understanding through advanced object context analysis in image

Advancements in computer vision have primarily concentrated on interpreting visual data, often overlooking the significance of contextual differences across various regions within images. In contrast, our research introduces a model for indoor scene recognition that pivots towards the ‘attention’ paradigm. This model views attention as a response to the stimulus image properties, suggesting that focus is ‘pulled’ towards the most visually salient zones within an image, as represented in a saliency map. Attention is directed towards these zones based on uninterpreted semantic features of the image, such as luminance contrast, color, shape, and edge orientation. This neurobiologically plausible and computationally tractable approach offers a more nuanced understanding of scenes by prioritizing zones solely based on their image properties. The proposed model enhances scene understanding through an in-depth analysis of the object context in images. Scene recognition is achieved by extracting features from selected regions of interest within individual image frames using patch-based object detection techniques, thus generating distinctive feature descriptors for the identified objects of interest. The resulting feature descriptors are then subjected to semantic embedding, which uses distributed representations to transform the sparse feature vectors into dense semantic vectors within a learned latent space. This enables subsequent classification tasks by machine learning models trained on embedded semantic representations. This model was evaluated on three image datasets: UIUC Sports-8, PASCAL VOC - Visual Object Classes, and a proprietary image set created by the authors. Compared to state-of-the-art methods, this paper presents a more robust approach to the abstraction and generalization of interior scenes. This approach has demonstrated superior accuracy with our novel model over existing models. Consequently, this has led to an improvement in the classification of scenes in the selected indoor environments. Our code is published here: https://github.com/sebastianlop8/Semantic-Scene-Object-Context-Analysis.git