A system for trading goods over a computer network based on image processing includes an image processor that analyzes a picture received from a user to build a description of entities in the picture and correlations among the entities, a description interpreter that analyzes the description to determine an underlying message in the picture and detect information regarding the user's interests, emotions and affinities to goods and services, wherein the information is stored with the user's profile, the user's list of items to trade, and the user's past transaction history, and a trade matching component that uses the user's profile and produces a ranked list of other users with items to trade, or intermediary trade steps, when another user initiates a new trading transaction.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for trading goods over a computer network based on image processing, comprising: an image processor that analyzes a picture received from a user to build a description of entities in the picture and correlations among the entities; a description interpreter that analyzes the description to determine an underlying message in the picture and detect information regarding the user's interests, emotions and affinities to goods and services, wherein the information is stored with the user's profile, the user's list of items to trade, and the user's past transaction history; and a trade matching component that uses the user's profile and produces a ranked list of other users with items to trade, or intermediary trade steps, when another user initiates a new trading transaction. 2. The system of claim 1, further comprising a database that stores the information regarding the user's interests, emotions and affinities to goods and services, with the user's profile, the user's list of items to trade, and the user's past transaction history. 3. The system of claim 1, wherein the description interpreter uses a machine learning algorithm that detects the user's interests, emotions and affinities to goods and services, wherein the machine learning algorithm is trained using descriptions and interpretations of a set of pictures to produce an inference function that can analyze new pictures from user sources. 4. The system of claim 1, wherein the description interpreter uses natural language processing to analyze the description. 5. The system of claim 1, wherein the image processor uses computer vision algorithms to build the description of entities in the picture and correlations among the entities, wherein said computer vision algorithms include conditional random fields for image segmentation and label prediction, convolution neural network for feature extraction, and a support vector machine for classification. 6. The system of claim 1, further comprising a dictionary, wherein the description interpreter uses a text analyzer to extract and evaluate each word in the description created by the image processor, compare each word with the dictionary, and evaluate and weight each word based on a pre-defined set of categories that define the user's interests, emotions and affinities to goods and services. 7. A non-transitory program storage device readable by a computer, tangibly embodying a program of instructions executed by the computer to perform the method steps for detecting intentions and motivations associated with items to trade over a computer network, the method comprising the steps of: analyzing a picture received from a user to detect information items regarding the user's interests, emotions and affinities to goods and services; detecting for each information item and associated picture a list of attributes that express sentiment, personal motivations, values and life events that affect the user's trading decision for each item; calculating an affinity/emotion score for each item in the item's list of attributes and classifying the score into predefined set of classes; and adding each item and its associated affinity/emotion score to a list of items that the user is interested in trading. 8. The computer readable program storage device of claim 7, wherein analyzing the picture comprises building a description of entities in the picture and correlations among the entities, and analyzing the description to detect an underlying message in the picture and information regarding the user's interests, emotions and affinities to goods and services. 9. The computer readable program storage device of claim 8, wherein the description is analyzed using natural language processing. 10. The computer readable program storage device of claim 8, wherein building a description of entities in the picture and correlations among the entities comprises using computer vision algorithms to build the description of entities in the picture and correlations among the entities, wherein said computer vision algorithms include conditional random fields for image segmentation and label prediction, convolution neural network for feature extraction, and a support vector machine for classification. 11. The computer readable program storage device of claim 8, the method further comprising using a text analyzer to extract and evaluate each word in the description created by the image processor, compare each word with a dictionary, and evaluate and weight each word based on a pre-defined set of categories that define the user's interests, emotions and affinities to goods and services. 12. The computer readable program storage device of claim 8, wherein analyzing the description comprises using a machine learning algorithm that detects the user's interests, emotions and affinities to goods and services, wherein the machine learning algorithm is trained using descriptions and interpretations of a set of pictures to produce an inference function that can analyze new pictures from user sources. 13. A non-transitory program storage device readable by a computer, tangibly embodying a program of instructions executed by the computer to perform the method steps for trading on a computer network, the method comprising the steps of: receiving from a first user a request to trade a first item; retrieving a first list of users with intention to acquire the first item from the first user; ranking users in the first list of users based on a user affinity/emotion score for each user, wherein the user affinity/emotion score is higher than a first predefined threshold; retrieving a first list of items that the first user has intention to acquire; ranking items in the first list of items based on an item affinity/emotion score for each item, wherein the item affinity/emotion score is higher than a second predefined threshold; retrieving for each user in the first list of users a second list of items that each said user has to trade; adding a second item in the second list of items, the user and affinity/emotion score associated with said second item to a trade list, if said second item is also in the first list of items and if the item affinity/emotion score of said second item is higher than the second predetermined threshold; and ranking the items in the trade list based on the item affinity/emotion score of each item. 14. The computer readable program storage device of claim 13, the method further comprising broadcasting the ranked list of items to each user in the trade list. 15. The computer readable program storage device of claim 13, the method further comprising, if the second item in the second list of items is not in the first list of items, retrieving a second list of users with intention to acquire the second item; ranking each user in the second list of users based on a user affinity/emotion score for each said user, wherein the user affinity/emotion score higher than the first threshold; retrieving for each user in the second list of users a third list of items that each said user has to trade; adding an item in the third list of items, the user and affinity/emotion score associated with said item to the trade list, if said item is also in the first list of items, and ranking the items in the trade list based on the affinity/emotion score of each item.