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.
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.
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