Shadow segmentation and tracking in real-world conditions

Visual information, in the form of images and video, comes from the interaction of light with objects. Illumination is a fundamental element of visual information. Detecting and interpreting illumination effects is part of our everyday life visual experience. Shading for instance allows us to perceive the three-dimensional nature of objects. Shadows are particularly salient cues for inferring depth information. However, we do not make any conscious or unconscious effort to avoid them as if they were an obstacle when we walk around. Moreover, when humans are asked to describe a picture, they generally omit the presence of illumination effects, such as shadows, shading, and highlights, to give a list of objects and their relative position in the scene. Processing visual information in a way that is close to what the human visual system does, thus being aware of illumination effects, represents a challenging task for computer vision systems. Illumination phenomena interfere in fact with fundamental tasks in image analysis and interpretation applications, such as object extraction and description. On the other hand, illumination conditions are an important element to be considered when creating new and richer visual content that combines objects from different sources, both natural and synthetic. When taken into account, illumination effects can play an important role in achieving realism. Among illumination effects, shadows are often integral part of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In this thesis, the problem of extracting shadows from digital images is discussed. A new analysis method for the segmentation of cast shadows in still and moving images without the need of human supervision is proposed. The problem of separating moving cast shadows from moving objects in image sequences is particularly relevant for an always wider range of applications, ranging from video analysis to video coding, and from video manipulation to interactive environments. Therefore, particular attention has been dedicated to the segmentation of shadows in video. The validity of the proposed approach is however also demonstrated through its application to the detection of cast shadows in still color images. Shadows are a difficult phenomenon to model. Their appearance changes with changes in the appearance of the surface they are cast upon. It is therefore important to exploit multiple constraints derived from the analysis of the spectral, geometric and temporal properties of shadows to develop effective techniques for their extraction. The proposed method combines an analysis of color information and of photometric invariant features to a spatio-temporal verification process. With regards to the use of color information for shadow analysis, a complete picture of the existing solutions is provided, which points out the fundamental assumptions, the adopted color models and the link with research problems such as computational color constancy and color invariance. The proposed spatial verification does not make any assumption about scene geometry nor about object shape. The temporal analysis is based on a novel shadow tracking technique. On the basis of the tracking results, a temporal reliability estimation of shadows is proposed which allows to discard shadows which do not present time coherence. The proposed approach is general and can be applied to a wide class of applications and input data. The proposed cast shadow segmentation method has been evaluated on a number of different video data representing indoor and outdoor real-world environments. The obtained results have confirmed the validity of the approach, in particular its ability to deal with different types of content and its robustness to different physically important independent variables, and have demonstrated the improvement with respect to the state of the art. Examples of application of the proposed shadow segmentation tool to the enhancement of video object segmentation, tracking and description operations, and to video composition, have demonstrated the advantages of a shadow-aware video processing.

Learning the structure of image collections with latent aspect models

Florent Monay Michaud

The approach to indexing an image collection depends on the type of data to organize. Satellite images are likely to be searched with latitude and longitude coordinates, medical images are often searched with an image example that serves as a visual query, and personal image collections are generally browsed by event. A more general retrieval scenario is based on the use of textual keywords to search for images containing a specific object, or representing a given scene type. This requires the manual annotation of each image in the collection to allow for the retrieval of relevant visual information based on a text query. This time-consuming and subjective process is the current price to pay for a reliable and convenient text-based image search. This dissertation investigates the use of probabilistic models to assist the automatic organization of image collections, attempting to link the visual content of digital images with a potential textual description. Relying on robust, patch-based image representations that have proven to capture a variety of visual content, our work proposes to model images as mixtures of latent aspects. These latent aspects are defined by multinomial distributions that capture patch co-occurrence information observed in the collection. An image is not represented by the direct count of its constituting elements, but as a mixture of latent aspects that can be estimated with principled, generative unsupervised learning methods. An aspect-based image representation therefore incorporates contextual information from the whole collection that can be exploited. This emerging concept is explored for several fundamental tasks related to image retrieval – namely classification, clustering, segmentation, and annotation – in what represents one of the first coherent and comprehensive study of the subject. We first investigate the possibility of classifying images based on their estimated aspect mixture weights, interpreting latent aspect modeling as an unsupervised feature extraction process. Several image categorization tasks are considered, where images are classified based on the present objects or according to their global scene type. We demonstrate that the concept of latent aspects allows to take advantage of non-labeled data to infer a robust image representation that achieves a higher classification performance than the original patch-based representation. Secondly, further exploring the concept, we show that aspects can correspond to an interesting soft clustering of an image collection that can serve as a browsing structure. Images can be ranked given an aspect, illustrating the corresponding co-occurrence context visually. In the third place, we derive a principled method that relies on latent aspects to classify image patches into different categories. This produces an image segmentation based on the resulting spatial class-densities. We finally propose to model images and their caption with a single aspect model, merging the co-occurrence contexts of the visual and the textual modalities in different ways. Once a model has been learned, the distribution of words given an unseen image is inferred based on its visual representation, and serves as textual indexing. Overall, we demonstrate with extensive experiments that the co-occurrence context captured by latent aspects is suitable for the above mentioned tasks, making it a promising approach for multimedia indexing.

EPFL2007

Learning the structure of image collections with latent aspect models

Florent Monay Michaud

The approach to indexing an image collection depends on the type of data to organize. Satellite images are likely to be searched with latitude and longitude coordinates, medical images are often searched with an image example that serves as a visual query, and personal image collections are generally browsed by event. A more general retrieval scenario is based on the use of textual keywords to search for images containing a specific object, or representing a given scene type. This requires the manual annotation of each image in the collection to allow for the retrieval of relevant visual information based on a text query. This time-consuming and subjective process is the current price to pay for a reliable and convenient text-based image search. This dissertation investigates the use of probabilistic models to assist the automatic organization of image collections, attempting to link the visual content of digital images with a potential textual description. Relying on robust, patch-based image representations that have proven to capture a variety of visual content, our work proposes to model images as mixtures of \emph{latent aspects}. These latent aspects are defined by multinomial distributions that capture patch co-occurrence information observed in the collection. An image is not represented by the direct count of its constituting elements, but as a mixture of latent aspects that can be estimated with principled, generative unsupervised learning methods. An aspect-based image representation therefore incorporates contextual information from the whole collection that can be exploited. This emerging concept is explored for several fundamental tasks related to image retrieval - namely classification, clustering, segmentation, and annotation - in what represents one of the first coherent and comprehensive study of the subject. We first investigate the possibility of classifying images based on their estimated aspect mixture weights, interpreting latent aspect modeling as an unsupervised feature extraction process. Several image categorization tasks are considered, where images are classified based on the present objects or according to their global scene type. We demonstrate that the concept of latent aspects allows to take advantage of non-labeled data to infer a robust image representation that achieves a higher classification performance than the original patch-based representation. Secondly, further exploring the concept, we show that aspects can correspond to an interesting soft clustering of an image collection that can serve as a browsing structure. Images can be ranked given an aspect, illustrating the corresponding co-occurrence context visually. In the third place, we derive a principled method that relies on latent aspects to classify image patches into different categories. This produces an image segmentation based on the resulting spatial class-densities. We finally propose to model images and their caption with a single aspect model, merging the co-occurrence contexts of the visual and the textual modalities in different ways. Once a model has been learned, the distribution of words given an unseen image is inferred based on its visual representation, and serves as textual indexing. Overall, we demonstrate with extensive experiments that the co-occurrence context captured by latent aspects is suitable for the above mentioned tasks, making it a promising approach for multimedia indexing.

École Polytechnique Fédérale de Lausanne2007

Learning the structure of image collections with latent aspect models

Florent Monay Michaud

The approach to indexing an image collection depends on the type of data to organize. Satellite images are likely to be searched with latitude and longitude coordinates, medical images are often searched with an image example that serves as a visual query, and personal image collections are generally browsed by event. A more general retrieval scenario is based on the use of textual keywords to search for images containing a specific object, or representing a given scene type. This requires the manual annotation of each image in the collection to allow for the retrieval of relevant visual information based on a text query. This time-consuming and subjective process is the current price to pay for a reliable and convenient text-based image search. This dissertation investigates the use of probabilistic models to assist the automatic organization of image collections, attempting to link the visual content of digital images with a potential textual description. Relying on robust, patch-based image representations that have proven to capture a variety of visual content, our work proposes to model images as mixtures of \emph{latent aspects}. These latent aspects are defined by multinomial distributions that capture patch co-occurrence information observed in the collection. An image is not represented by the direct count of its constituting elements, but as a mixture of latent aspects that can be estimated with principled, generative unsupervised learning methods. An aspect-based image representation therefore incorporates contextual information from the whole collection that can be exploited. This emerging concept is explored for several fundamental tasks related to image retrieval - namely classification, clustering, segmentation, and annotation - in what represents one of the first coherent and comprehensive study of the subject. We first investigate the possibility of classifying images based on their estimated aspect mixture weights, interpreting latent aspect modeling as an unsupervised feature extraction process. Several image categorization tasks are considered, where images are classified based on the present objects or according to their global scene type. We demonstrate that the concept of latent aspects allows to take advantage of non-labeled data to infer a robust image representation that achieves a higher classification performance than the original patch-based representation. Secondly, further exploring the concept, we show that aspects can correspond to an interesting soft clustering of an image collection that can serve as a browsing structure. Images can be ranked given an aspect, illustrating the corresponding co-occurrence context visually. In the third place, we derive a principled method that relies on latent aspects to classify image patches into different categories. This produces an image segmentation based on the resulting spatial class-densities. We finally propose to model images and their caption with a single aspect model, merging the co-occurrence contexts of the visual and the textual modalities in different ways. Once a model has been learned, the distribution of words given an unseen image is inferred based on its visual representation, and serves as textual indexing. Overall, we demonstrate with extensive experiments that the co-occurrence context captured by latent aspects is suitable for the above mentioned tasks, making it a promising approach for multimedia indexing.

IDIAP2007

Learning the structure of image collections with latent aspect models

Florent Monay Michaud

EPFL2007

Learning the structure of image collections with latent aspect models

Florent Monay Michaud

The approach to indexing an image collection depends on the type of data to organize. Satellite images are likely to be searched with latitude and longitude coordinates, medical images are often searched with an image example that serves as a visual query, and personal image collections are generally browsed by event. A more general retrieval scenario is based on the use of textual keywords to search for images containing a specific object, or representing a given scene type. This requires the manual annotation of each image in the collection to allow for the retrieval of relevant visual information based on a text query. This time-consuming and subjective process is the current price to pay for a reliable and convenient text-based image search. This dissertation investigates the use of probabilistic models to assist the automatic organization of image collections, attempting to link the visual content of digital images with a potential textual description. Relying on robust, patch-based image representations that have proven to capture a variety of visual content, our work proposes to model images as mixtures of \emph{latent aspects}. These latent aspects are defined by multinomial distributions that capture patch co-occurrence information observed in the collection. An image is not represented by the direct count of its constituting elements, but as a mixture of latent aspects that can be estimated with principled, generative unsupervised learning methods. An aspect-based image representation therefore incorporates contextual information from the whole collection that can be exploited. This emerging concept is explored for several fundamental tasks related to image retrieval - namely classification, clustering, segmentation, and annotation - in what represents one of the first coherent and comprehensive study of the subject. We first investigate the possibility of classifying images based on their estimated aspect mixture weights, interpreting latent aspect modeling as an unsupervised feature extraction process. Several image categorization tasks are considered, where images are classified based on the present objects or according to their global scene type. We demonstrate that the concept of latent aspects allows to take advantage of non-labeled data to infer a robust image representation that achieves a higher classification performance than the original patch-based representation. Secondly, further exploring the concept, we show that aspects can correspond to an interesting soft clustering of an image collection that can serve as a browsing structure. Images can be ranked given an aspect, illustrating the corresponding co-occurrence context visually. In the third place, we derive a principled method that relies on latent aspects to classify image patches into different categories. This produces an image segmentation based on the resulting spatial class-densities. We finally propose to model images and their caption with a single aspect model, merging the co-occurrence contexts of the visual and the textual modalities in different ways. Once a model has been learned, the distribution of words given an unseen image is inferred based on its visual representation, and serves as textual indexing. Overall, we demonstrate with extensive experiments that the co-occurrence context captured by latent aspects is suitable for the above mentioned tasks, making it a promising approach for multimedia indexing.

École Polytechnique Fédérale de Lausanne2007

Learning the structure of image collections with latent aspect models

Florent Monay Michaud

The approach to indexing an image collection depends on the type of data to organize. Satellite images are likely to be searched with latitude and longitude coordinates, medical images are often searched with an image example that serves as a visual query, and personal image collections are generally browsed by event. A more general retrieval scenario is based on the use of textual keywords to search for images containing a specific object, or representing a given scene type. This requires the manual annotation of each image in the collection to allow for the retrieval of relevant visual information based on a text query. This time-consuming and subjective process is the current price to pay for a reliable and convenient text-based image search. This dissertation investigates the use of probabilistic models to assist the automatic organization of image collections, attempting to link the visual content of digital images with a potential textual description. Relying on robust, patch-based image representations that have proven to capture a variety of visual content, our work proposes to model images as mixtures of \emph{latent aspects}. These latent aspects are defined by multinomial distributions that capture patch co-occurrence information observed in the collection. An image is not represented by the direct count of its constituting elements, but as a mixture of latent aspects that can be estimated with principled, generative unsupervised learning methods. An aspect-based image representation therefore incorporates contextual information from the whole collection that can be exploited. This emerging concept is explored for several fundamental tasks related to image retrieval - namely classification, clustering, segmentation, and annotation - in what represents one of the first coherent and comprehensive study of the subject. We first investigate the possibility of classifying images based on their estimated aspect mixture weights, interpreting latent aspect modeling as an unsupervised feature extraction process. Several image categorization tasks are considered, where images are classified based on the present objects or according to their global scene type. We demonstrate that the concept of latent aspects allows to take advantage of non-labeled data to infer a robust image representation that achieves a higher classification performance than the original patch-based representation. Secondly, further exploring the concept, we show that aspects can correspond to an interesting soft clustering of an image collection that can serve as a browsing structure. Images can be ranked given an aspect, illustrating the corresponding co-occurrence context visually. In the third place, we derive a principled method that relies on latent aspects to classify image patches into different categories. This produces an image segmentation based on the resulting spatial class-densities. We finally propose to model images and their caption with a single aspect model, merging the co-occurrence contexts of the visual and the textual modalities in different ways. Once a model has been learned, the distribution of words given an unseen image is inferred based on its visual representation, and serves as textual indexing. Overall, we demonstrate with extensive experiments that the co-occurrence context captured by latent aspects is suitable for the above mentioned tasks, making it a promising approach for multimedia indexing.

IDIAP2007