We continue discussing Convolutional Neural Network (CNN) in image and object embedding in a shared space, shape signature and image retrieval.
The 2D distance matrix formed from word embeddings in text documents that is dimensionality reduced and classified using the softmax function  is similarly put to use with the distance matrix between 3D models although the feature vector, distance calculation, algorithm and error function are different. Neural nets are applied to embedding in both text documents and images.
CNN has the ability to separate an image into various layers of abstraction while capturing different features and elements. This lets CNN to be utilized for different learning tasks where the tasks may differ on the focus they require. It is this adaptive ability of CNN that is leveraged for joint embedding.  The CNN is first trained to map an image depicting an object similar to a shape to a corresponding point in the embedding space such that the position of the point for the image is closer to the point for the shape. During this training, the CNN discovers latent connection between that exists between an image and the object it features. Then when a test image is presented, the latent connection helps to place that image in the embedding space closer to the object it features.
Moreover, CNN can generalize from different tasks. This makes it useful to repurpose a well-trained network. Since it learns from a high dimensional space, CNN can differentiate even similar images for a variety of tasks.

As we know with Euclidean distance, the chi-square measure, which is the sum of squares of errors, gives a good indication of how close the objects are to the mean. Therefore it is a measure for the goodness of fit. The principle is equally applicable to embedding space. By using a notion of errors, we can make sure that the shapes and images embedded in the space do not violate the intra member distances. The only variation the authors applied to this measure is the use of Sammon error instead of the chi-square because it encourages the preservation of the structure of local neighborhoods while embedding. The joing embedding space is a Euclidean space of lower dimension while the shapes and the images are represented in the original high dimensional space.

#proposal for login screens:  https://1drv.ms/w/s!Ashlm-Nw-wnWtWiX5uxOG6zc4a8K
Thumbnail images instead of literals can also enhance the login screens. Avatars are an example of this.

We continue discussing Convolutional Neural Network (CNN) in image and object embedding in a shared space, shape signature and image retrieval.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well.  In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The embedding space is where both real-world images and shapes co-exist.  The space organizes latent objects between images and shapes. In order to do this, the objects are initialized from a set of 3D models.  The are pure and complete representation of objects. They don't suffer from the noise in images. The distance between 3D models is both informative and precise. With the help of 3D models, the embedding space becomes robust.
The shape distance metric computes the similarity between two shapes by the aggregate of similarities among corresponding views. This method is called Light field descriptor. The input is a set of 3D shapes although two would do.The shapes are aligned by applying a transformation using a rotation matrix and a translation vector. Then they are projected from k viewpoints to generate projection images
The CNN uses this distance metric to form a pairwise comparison between the 3D models. Since the metric is informative and accurate, the models can be organized in space along increasing dimensions.
The 2D distance matrix formed from word embeddings in text documents that is dimensionality reduced and classified using the softmax function  is similarly put to use with the distance matrix between 3D models although the feature vector, distance calculation, algorithm and error function are different. Neural nets are applied to embedding in both text documents and images.
CNN has the ability to separate an image into various layers of abstraction while capturing different features and elements. This lets CNN to be utilized for different learning tasks where the tasks may differ on the focus they require. It is this adaptive ability of CNN that is leveraged for joint embedding.  The CNN is first trained to map an image depicting an object similar to a shape to a corresponding point in the embedding space such that the position of the point for the image is closer to the point for the shape. During this training, the CNN discovers latent connection between that exists between an image and the object it features. Then when a test image is presented, the latent connection helps to place that image in the embedding space closer to the object it features.
Moreover, CNN can generalize from different tasks. This makes it useful to repurpose a well-trained network. Since it learns from a high dimensional space, CNN can differentiate even similar images for a variety of tasks.
#proposal for login screens:  https://1drv.ms/w/s!Ashlm-Nw-wnWtWiX5uxOG6zc4a8K
Thumbnail images instead of literals can also enhance the login screens. Avatars are an example of this.

Today also we continue discussing  Convolutional Neural Network (CNN) in image and object embedding in a shared space, shape signature and image retrieval.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well.  In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The embedding space is where both real-world images and shapes co-exist.  The space organizes latent objects between images and shapes. In order to do this, the objects are initialized from a set of 3D models.  The are pure and complete representation of objects. They don't suffer from the noise in images. The distance between 3D models is both informative and precise. With the help of 3D models, the embedding space becomes robust.
The shape distance metric computes the similarity between two shapes by the aggregate of similarities among corresponding views. This method is called Light field descriptor. The input is a set of 3D shapes although two would do.The shapes are aligned by applying a transformation using a rotation matrix and a translation vector. Then they are projected from k viewpoints to generate projection images
The CNN uses this distance metric to form a pairwise comparison between the 3D models. Since the metric is informative and accurate, the models can be organized in space along increasing dimensions.
The 2D distance matrix formed from word embeddings in text documents that is dimensionality reduced and classified using the softmax function  is similarly put to use with the distance matrix between 3D models although the feature vector, distance calculation, algorithm and error function are different. Neural nets are applied to embedding in both text documents and images.
#proposal for login screens:  https://1drv.ms/w/s!Ashlm-Nw-wnWtWiX5uxOG6zc4a8K

Today also we continue discussing  Convolutional Neural Network (CNN) in image and object embedding in a shared space, shape signature and image retrieval.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well.  In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The embedding space is where both real-world images and shapes co-exist.  The space organizes latent objects between images and shapes. In order to do this, the objects are initialized from a set of 3D models.  The are pure and complete representation of objects. They don't suffer from the noise in images. The distance between 3D models is both informative and precise. With the help of 3D models, the embedding space becomes robust.
The shape distance metric computes the similarity between two shapes by the aggregate of similarities among corresponding views. This method is called Light field descriptor. The input is a set of 3D shapes although two would do.The shapes are aligned by applying a transformation using a rotation matrix and a translation vector. Then they are projected from k viewpoints to generate projection images
The CNN uses this distance metric to form a pairwise comparison between the 3D models. Since the metric is informative and accurate, the models can be organized in space along increasing dimensions.
The 2D distance matrix formed from word embeddings in text documents that is dimensionality reduced and classified using the softmax function  is similarly put to use with the distance matrix between 3D models although the feature vector, distance calculation, algorithm and error function are different. Neural nets are applied to embedding in both text documents and images. 

Today also we continue discussing  Convolutional Neural Network (CNN) in image and object embedding in a shared space, shape signature and image retrieval.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well.  In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The embedding space is where both real-world images and shapes co-exist.  The space organizes latent objects between images and shapes. In order to do this, the objects are initialized from a set of 3D models.  The are pure and complete representation of objects. They don't suffer from the noise in images. The distance between 3D models is both informative and precise. With the help of 3D models, the embedding space becomes robust.
The shape distance metric computes the similarity between two shapes by the aggregate of similarities among corresponding views. This method is called Light field descriptor. The input is a set of 3D shapes although two would do.The shapes are aligned by applying a transformation using a rotation matrix and a translation vector. Then they are projected from k viewpoints to generate projection images
The CNN uses this distance metric to form a pairwise comparison between the 3D models. Since the metric is informative and accurate, the models can be organized in space along increasing dimensions.
The 2D distance matrix formed from word embeddings in text documents that is dimensionality reduced and classified using the softmax function  is similarly put to use with the distance matrix between 3D models although the feature vector, distance calculation, algorithm and error function are different.

Today also we continue discussing  Convolutional Neural Network (CNN) in image and object embedding in a shared space, shape signature and image retrieval.
The purpose of the embedding is to map an image to a point in the embedding space so that it is close to a point attributed to a 3D model of a similar object. A large amount of training data consisting of images synthesized from 3D shapes is used to train the CNN.
By using synthesized images, the embedding space is computed from clean 3D models without noise. This enables better object similarities. In addition 2D shape views are tossed in which boost the shape matching. This use of embedding space is a novel approach and enables a better domain for subsequent image and shape retrievals. Moreover the embedding space does away with linear classifiers. This yields robust comparision of real world images to 3D models. Previously, this was susceptible to image nuisance factors from real world images and the linear classifiers could not keep up. On the other hand the use of CNN mitigates this because it does better with image invariance learning  - a technique that focuses on the salient invariant embedded objects rather than noise.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well.  In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The shared embedding space allows new images to be introduced into the space anytime. The CNN merely takes the new image as input and uses the output.  If we have to embed new shapes, then it must preserve the pairwise distances between the added shape and the existing shapes within the space. The embedding space is constructed from an initial collection of 3D shapes. Introducing new shapes subsequently tends to violate the space. Instead if we could treat this an optimization problem, then we can preserve the criteria for the embedding space.
The embedding space is where both real-world images and shapes co-exist.  The space organizes latent objects between images and shapes. In order to do this, the objects are initialized from a set of 3D models.  The are pure and complete representation of objects. They don't suffer from the noise in images. The distance between 3D models is both informative and precise. With the help of 3D models, the embedding space becomes robust.
The shape distance metric computes the similarity between two shapes by the aggregate of similarities among corresponding views. This method is called Light field descriptor. The input is a set of 3D shapes although two would do.The shapes are aligned by applying a transformation using a rotation matrix and a translation vector. Then they are projected from k viewpoints to generate projection images
The CNN uses this distance metric to form a pairwise comparison between the 3D models. Since the metric is informative and accurate, the models can be organized in space along increasing dimensions.

Today also we continue discussing  Convolutional Neural Network (CNN) in image and object embedding in a shared space, shape signature and image retrieval.
The purpose of the embedding is to map an image to a point in the embedding space so that it is close to a point attributed to a 3D model of a similar object. A large amount of training data consisting of images synthesized from 3D shapes is used to train the CNN.
By using synthesized images, the embedding space is computed from clean 3D models without noise. This enables better object similarities. In addition 2D shape views are tossed in which boost the shape matching. This use of embedding space is a novel approach and enables a better domain for subsequent image and shape retrievals. Moreover the embedding space does away with linear classifiers. This yields robust comparision of real world images to 3D models. Previously, this was susceptible to image nuisance factors from real world images and the linear classifiers could not keep up. On the other hand the use of CNN mitigates this because it does better with image invariance learning  - a technique that focuses on the salient invariant embedded objects rather than noise.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well.  In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The shared embedding space allows new images to be introduced into the space anytime. The CNN merely takes the new image as input and uses the output.  If we have to embed new shapes, then it must preserve the pairwise distances between the added shape and the existing shapes within the space. The embedding space is constructed from an initial collection of 3D shapes. Introducing new shapes subsequently tends to violate the space. Instead if we could treat this an optimization problem, then we can preserve the criteria for the embedding space.
The embedding space is where both real-world images and shapes co-exist.  The space organizes latent objects between images and shapes. In order to do this, the objects are initialized from a set of 3D models.  The are pure and complete representation of objects. They don't suffer from the noise in images. The distance between 3D models is both informative and precise. With the help of 3D models, the embedding space becomes robust.
The shape distance metric computes the similarity between two shapes by the aggregate of similarities among corresponding views. This method is called Light field descriptor. The input is a set of 3D shapes although two would do.The shapes are aligned by applying a transformation using a rotation matrix and a translation vector. Then they are projected from k viewpoints to generate projection images